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Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Chongqing University of Posts & Telecommunications (Natural Science Edition) is the property of Chongqing University of Posts & Telecommunications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The traditional medical image segmentation network has a large number of parameters and slow computing speed, and cannot applies effectively to the real-time detection technology. To address this issue, this paper proposed a lightweight medical image segmentation network called SPTFormer. Firstly, this network constructed a self-blocking Transformer module, which reshaped the feature map through an adaptive blocking strategy and utilized parallel computing to improve the attention operation speed while paying attention to local detail features. Secondly, this network constructed an SR-CNN module, which used the shift-restored operation to improve the ability to capture local spatial information. By experimenting on ISIC 2018, BUSI, CVC-ClinicDB and 2018 data science bowl, compared with the TransUNet model based on Transformer, the accuracy of the proposed network improves by 4.28%, 3.74%, 6.50%, and 1. 16%, respectively, the GPU computation time reduces by 58%. The proposed network has better performance in medical image segmentation applications, which can well balance the network accuracy and complexity, and provides a new solution for real-time computer-aided diagnosis. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Chinese Journal of Liquid Crystal & Displays is the property of Chinese Journal of Liquid Crystal & Displays and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Automatic segmentation of retinal vessels plays an important role in computer-aided diagnosis of ophthalmology and cardiovascular diseases. Attention mechanism can improve the efficiency and accuracy of image feature extraction in classical neural network models, so attention mechanism is widely used in retinal vessel segmentation models. This paper firstly reviews the commonly used datasets and evaluation metrics for retinal vessel segmentation, subsequently, attention mechanisms are categorized into two types: selective attention mechanisms and self-attention mechanisms, based on their working principles. Meanwhile, according to the data domain of computer vision tasks, attention methods are divided into three categories: channel attention, spatial attention, and mixed attention. Combined with the task of retinal vessel segmentation, the paper highlights the specific applications of representative attention models of these three types and conducts performance comparisons and evaluations of relevant models. Finally, the problems of attention mechanism and the development trend in the future are discussed. [ABSTRACT FROM AUTHOR]

Breast cancer is the most common malignant tumor in women and its early detection is decisive. Breast imaging plays an important role in early detection of breast cancer as well as monitoring and evaluation during treatment, but manual detection of medical images is usually time-consuming and labor-intensive. Recently, deep learning algorithms have made significant progress in early breast cancer diagnosis. By combing the relevant literature in recent years, a systematic review of the application of deep learning techniques in breast cancer diagnosis with different imaging modalities is conducted, aiming to provide a reference for in-depth research on deep learning-based breast cancer diagnosis. Firstly, four breast cancer imaging modalities, namely mammography, ultrasonography, magnetic resonance imaging and positron emission tomography, are outlined and briefly compared, and the public datasets corresponding to multiple imaging modalities are listed. Focusing on the different tasks (lesion detection, segmentation and classification) of deep learning architectures based on the above four different imaging modalities, a systematic review of the algorithms is conducted, and the performance of each algorithm, improvement ideas, and their advantages and disadvantages are compared and analyzed. Finally, the problems of the existing techniques are analyzed and the future development direction is prospected with respect to the limitations of the current work.

Copyright of Journal of Frontiers of Computer Science & Technology is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Graphics is the property of Journal of Graphics Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Rapid and accurate segmentation of COVID-19 lesions from CT images is an important step to realize computer-assisted diagnosis and treatment of COVID-19. Therefore, a CT image segmentation method of COVID-19 lesions based on dual-attention mechanism is proposed. Firstly, the attention gate module is introduced to enhance the focus on the focal region in space and reduce the influence of image brightness imbalance and low contrast on the segmentation accuracy. Secondly, the SE-Res module combined with residual element was introduced to enhance the channel of the lesion region, extract the fine structural features, and improve the segmentation performance of the network for the lesion shape change and ground glass boundary region. Experiments on the public datasets of COVID-19 show that the Dice, PPV and IoU achieved by the proposed method are 0.908 8, 0.915 2 and 0.858 9, respectively, which are 0.75%, 0.11% and 0.65%higher than previous studies, respectively. The proposed method can improve the segmentation accuracy of the lesions with large shape changes and ground glass boundaries, and the overall performance is better than current mainstream models. [ABSTRACT FROM AUTHOR]

Copyright of CT Theory & Applications is the property of Editorial Department of CT Theory & Applications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Skin diseases affect people's health and quality of life because of their high incidence, difficult diagnosis and apparent harm, coupled with insufficient medical resources. In recent years, with the development of computer-aided diagnosis (CAD) technology, single-modality CAD approaches have broken the limitations of traditional methods, such as strong subjectivity, and high missed-diagnosis and misdiagnosis rate, but failed to leverage the multi-modal information in real clinical scenarios. Multi-modality CAD methods help artificial intelligence models learn the clinical representations in a more complex and comprehensive manner, aiding dermatologists in making a more accurate diagnosis of skin diseases. This article introduces different types of skin lesion data commonly used in CAD methods, summarizes the single-modality/multi-modality methods based on related works in the field of CAD systems of skin diseases, and predicts possible future development trends of CAD technology, thus providing insights for mitigating the challenge on the diagnosis of skin diseases.

Copyright of Journal of Zhejiang University (Science Edition) is the property of Journal of Zhejiang University (Science Edition) Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The establishment of computer-aided diagnosis model based on C-TIRADS guidelines for thyroid nodule malignant risk stratification in line with China's national conditions has important application value, which can improve the standardization, standardization and homogeneity of thyroid nodule and differentiated thyroid cancer diagnosis, improve doctors' diagnosis efficiency and reduce labor intensity. In this paper, a multi-label target detection model based on deep learning is proposed to detect and recognize thyroid nodules, predict the malignant grade of thyroid nodules and the pathological characteristics of nodules. Through experimental comparison on the data set, Mask R-CNN is selected as the benchmark model for optimization and improvement, including using ResNet152-FPN to replace the original feature extraction network to improve the model's feature capabilities, design a new convolutional multi tag detector head structure to perform multi tag prediction on pathological features of nodules, customize the size and proportion of the model anchor frame based on medical prior knowledge to improve the positioning accuracy of the model, and finally design a transfer learning training scheme for the model to further improve model performance. The experimental results show that the recognition accuracy of the improved model for thyroid nodules is 94.4%, and the average recognition accuracy for thyroid nodule pathological features is 88.6%. [ABSTRACT FROM AUTHOR]

Copyright of Journal of China Medical University is the property of Journal of China Medical University Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Frontiers of Computer Science & Technology is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Objective To explore the feasibility of establishing a computer-aided diagnostic model of multi-stained pathological images in patients with non-inflammatory aortic medial degeneration(MD). Methods In this study, pathological sections of aortic surgical specimens for non-inflammatory lesions from patients with thoracic aortic aneurysms and dissections were retrospectively collected at the Beijing Anzhen Hospital, Capital Medical University from July to December 2018. The lesions were scanned under ×400 magnification as whole slide images(WSI) and then annotated by two pathologists. The annotated WSI images were randomly split into training and test sets in a 6:1 ratio. SE-EmbraceNet was used to train the data to construct a multi-classification model for MD of multi-stained pathology images, including intralamellar mucoid extracellular matrix accumulation (MEMA-I), translamellar mucoid extracellular matrix accumulation (MEMA-T), elastic fiber fragmentation and/or loss(EFFL) and smooth muscle cell nuclei loss (SMCNL). The classification effect of the model was evaluated based on the test set data, and the results were expressed in terms of accuracy, sensitivity, precision, and the F1 value. Results Totally 530 pathological slides of non-inflammatory aortic lesion surgical specimens from patients with aortic aneurysm and dissection were included. Extracted 5265 sets of images, each containing 5 stained pathological images of the same lesion site: HE staining, special staining (elastic fiber/VanGieson, Masson, Alcian blue/periodic acid Schiff) and smooth muscle actin staining. There were 4513 sets of training images, including 987 SMCNL, 2013 EFFL, 1337 MEMA-I, and 176 MEMA-T; and 752 test images including 166 SMCNL, 335 EFFL, 222 MEMA-I, and 29 MEMA-T. The overall performance of the model in the test set showed good results, with an accuracy of 96.54%(726/752). The model had the best classification performance for EFFL, with accuracy, sensitivity, precision, and F1 value all ≥98.51%. The model also had a great classification ability for SMCNL, with all evaluated indexes≥97.59%. Conclusion The multi-stained pathology image-based MD classification model constructed in this study has high classification accuracy and good generalization ability, which has the potential to be applied to assist in the diagnosis of the non-inflammatory aortic lesion.

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

BACKGROUND: Kidney CT image with poor quality shows similar gray scale to that of surrounding tissues on abdominal CT images. Therefore, it is difficult to segment the kidney accurately by traditional image segmentation method. OBJECTIVE: To assist the diagnosis of renal diseases and improve the accuracy of renal segmentation in CT images based on an improved geodesic active contour model. METHODS: Based on the comparative analysis of various traditional medical image segmentation algorithms, a kidney segmentation algorithm based on the improved geodesic active contour model was designed. The region of interest was delineated according to prior knowledge, and the initial contour of the kidney was obtained during the pretreatment stage. Based on the geodesic active contour model of the level set method, the boundary response of the kidney region was enhanced and the improved edge indicator function was used to make the contour curve evolution result closer to the real target boundary. RESULTS AND CONCLUSION: The mean Dice coefficient and mean overlap degree of 328 two-dimensional CT images of the kidney were 0.974 9 and 0.907 1, respectively, which were improved compared with other level set methods. Experimental results show that this model can improve the segmentation accuracy and efficiency of the kidney region in abdominal CT images. [ABSTRACT FROM AUTHOR]

Medical artificial intelligence (AI) applies AI technology to the medical field to meet clinical needs, combines with the experience of doctors, and relies on the computing, analysis and decision-making ability of AI to provide accurate intelligent assistance for clinical diagnosis and treatment. At present, medical AI is based on knowledge guided and data driven approaches, but each has its own advantages and disadvantages. Combining knowledge -guided AI with data- driven AI and utilizing their respective advantages is expected to break through the application bottle neck of medical AI and promote the development and innovation of medical AI. This paper summarizes the application of medical AI driven by knowledge guidance and data mining and looks forward to the future development direction, in order to promote the innovation and application of AI technology in the medical field. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Harbin University of Science & Technology is the property of Journal of Harbin University of Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Chinese Journal of Liquid Crystal & Displays is the property of Chinese Journal of Liquid Crystal & Displays and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Accurate diagnosis of tumor is very important for customizing treatment plans and predicting prognosis.Pathological diagnosis is considered the "gold standard" for tumor diagnosis,but the development of pathology still faces great challenges,such as the lack of pathologists,especially in underdeveloped areas and small hospitals,has led to long-term overload of pathologists.At the same time,pathological diagnosis relies heavily on the professional knowledge and diagnostic experience of pathologists,and this subjectivity of pathological diagnosis has led to a surge in diagnostic inconsistencies.The breakthrough of whole slide images (WSI) technology and deep learning methods provides new development opportunities for computer-aided diagnosis and prognosis prediction.Histopathological sections stained with hematoxylin-eosin (HE) can show cell morphology and tissue structure very well,and are simple to make,inexpensive,and widely used.What can be predicted from pathological images alone? After the deep learning method was applied to the field of pathological images,this question got a new answer.In this paper,we first summarize the overall research framework of tumor-related indicators prediction based on deep learning and pathological images.According to the development sequence of the overall research framework,it can be summarized into three progressive stages:WSI predictions based on manually selected single patch,WSI predictions based on majority voting,and WSI predictions with general applicability;Secondly,four supervised or weakly supervised learning methods commonly used in WSI prediction are briefly introduced:convolutional neural network (CNN),recurrent neural network (RNN),graph neural network (GNN),multiple instance learning (MIL).Then,we reviewed the related deep learning methods used in this field,what are the tumor-related indicators that can be predicted through pathological images,and the latest research progress.We mainly reviewed the literature from two aspects:predicting tumor-related indicators (tumor classification,tumor grading,tumor area recognition) that pathologists can read and recognize,and predicting tumor-related indicators (genetic variation prediction,molecular subtype prediction,treatment effect evaluation,survival time prediction) that pathologists cannot read and recognize.Finally,the general problems in this field are summarized,and the possible development direction in the future is suggested.

In the course of computer-aided cardiovascular disease diagnosis, field experience modeling usually being ignored.Hereafter the strategy to focus on the discovey and study of implicit knowledge was presented.The importance of implicit knowledge in knowledge engineering and its transformation process to explicit knowledge was discussed.Technical roadmap simulating physicians’ thinking by infusing rules inference and machine learning was introduced.And the computer-aided traditional Chinese medicine new prescription generating was analysed, that is, the machine gave a new prescription for a certain disease by simulating the thinking process of traditional Chinese medicine.It is hoped that this approach can provide a reference for the modeling of applications in other fields of artificial intelligence.

Copyright of CT Theory & Applications is the property of Editorial Department of CT Theory & Applications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Lung cancer ranks first in cancer mortality, and its early diagnosis and treatment can reduce the mortality rate of lung cancer patients. Deep learning can automatically extract nodule features, and complete the classification of benign, malignant and malignant grades of pulmonary nodules. Therefore, deep learning methods have become an important means of early diagnosis of lung cancer. This paper introduces the commonly used datasets, then systematically expounds the application of stacked denoising autoencoder (SDAE), deep belief network (DBN), generative adversarial network (GAN), convolutional neural network (CNN), recurrent neural network (RNN) and transfer learning techniques in the classification of benign and malignant pulmonary nodules. And then, the application of deep convolutional generative adversarial network (DCGAN), multi-scale convolutional neural network (MCNN), U-shaped network (U-Net) and ensemble learning techniques in the classification of malignant grades of pulmonary nodules is described. A comprehensive analysis of deep learning methods for pulmonary nodules is carried out, and the future research directions are prospected. [ABSTRACT FROM AUTHOR]

Objective To investigate the deep learning algorithm for automatic segmentation of pancreatic tissue from CT images. Methods Based on both 2D and 3D pancreatic segmentation networks, which extracted discriminative semantic information and three-dimensional spatial information from 82 CT images of pancreas samples (taken from NIH database) respectively, a novel hybrid network was designed for automatic segmentation of pancreatic tissues. Results The proposed method was validated using NIH datasets, and the Results of pancreas segmentation demonstrated accurately in detail and smooth in edge. The average Dice coefficient reached 83.2%, which exceeded the 2D and 3D benchmark Methods by 0.77% and 1.38%, respectively. Conclusion This method combines both advantages of 2D and 3D segmentation networks, and thus can be effectively applied to the accurate segmentation of pancreas, providing a quantitative reference for subsequent clinical diagnosis and treatment.

Accurate segmentation of prostate region is an important prerequisite to improve the accuracy of computer-aided prostate cancer diagnosis. In this work, a new and accurate prostate segmentation algorithm is proposed and tested. The new algorithm consists of 4 steps: reading T2-weighted magnetic resonance images, calculating local binary pattern (LBP) feature map of prostate magnetic resonance images by using an 8x5 LBP feature template, segmenting the feature map with the improved distance regularization level set evolution (DRLSE) algorithm, and extracting coarse contour of the prostate. A new energy function is constructed to extract local gray scale information and gradient information, and the coarse contour is iteratively developed into the final fine prostate contour on the basis of this new energy function. The algorithm was tested with the SPIE-AAPM-NCI Prostate MR Classification Challenge Database. The segmentation results of the proposed algorithm were compared with that of manual segmentation by doctors. The results showed that the Dice coefficient obtained by using the proposed algorithm was 0.94±0.01, with a relative volume difference (RVD) of -1.21%±2.44% and a 95% Hausdorff distance (HD) of 6.15±0.66 mm. Compared with the existing segmentation algorithms, the segmentation results obtained with the algorithm proposed in this paper are closer to the manual segmentation results.

Accurate classification of cancer is directly related to the choice of treatment options and prognosis. Pathological diagnosis is the gold standard for cancer diagnosis. The digitalization of pathological images and breakthroughs in deep learning have made computer-aided diagnosis and prediction about prognosis possible. In this paper, we first briefly describe four deep learning methods commonly used in this field, and then review the latest research progress in cancer classification based on deep learning and histopathological images. Finally, the general problems in this field are summarized, and the possible development direction in the future is suggested.

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Experimental Technology & Management is the property of Experimental Technology & Management Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of China Mechanical Engineering is the property of Editorial Board of China Mechanical Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Automatic detection of breast lesions is of great significance for subsequent computer-aided diagnosis. This paper combines three kinds of prior knowledge, i.e. background prior, adaptive center prior and frequency prior to propose a multi-saliency detection scheme, which is employed to detect breast lesions in ultrasound images. The scheme includes three stages: image preprocessing, saliency detection and saliency optimization. In addition, in order to improve the detection accuracy, it proposes an adaptive threshold segmentation method based on empirical cumulative distribution function and an improved adaptive center prior detection method. Experimental results indicate that the proposed multi-saliency detection scheme achieves the precision of 92.50%, recall of 87.05% and F-measure of 91.18%, which demonstrates better performance in detecting breast lesions in ultrasound images. [ABSTRACT FROM AUTHOR]

In the automatic analysis of retinal images, the location of optic disc and macula fovea is a prerequisite for computer aided diagnosis or screening of diabetic retinopathy. In this paper, a novel algorithm for locating the optic disc and macula fovea in retinal images based on YOLOv4-tiny is proposed and transplanted to field programmable gate array (FPGA) platform. Compared with the traditional method, the present work can not only locate the position of the disc and macula in retinal images simultaneously, quickly and precisely, but also be the first attempt to realize 38 layers mediumsized neural network through using high level synthesis (HLS) language and time division multiplexing technology. The COCO data set and 381 retinal images from the recognized Kaggle-Diabetic Retinopathy Detection competition are used in the training experiment. From the experimental results, the mean average precision (mAP) of optic disc and macular fovea localization after transplanting to FPGA platform is 96.11%, and it only takes 150.445 ms to detect an image on the FPGA system, which presents a good clinical application prospect in related fields. [ABSTRACT FROM AUTHOR]

Background and objective Lung cancer is the cancer with the highest mortality at home and abroad at present. The detection of lung nodules is a key step to reducing the mortality of lung cancer. Artificial intelligence-assisted diagnosis system presents as the state of the art in the area of nodule detection, differentiation between benign and malignant and diagnosis of invasive subtypes, however, a validation with clinical data is necessary for further application. Therefore, the aim of this study is to evaluate the effectiveness of artificial intelligence-assisted diagnosis system in predicting the invasive subtypes of early-stage lung adenocarcinoma appearing as pulmonary nodules. Methods Clinical data of 223 patients with early-stage lung adenocarcinoma appearing as pulmonary nodules admitted to the Lanzhou University Second Hospital from January 1st, 2016 to December 31th, 2021 were retrospectively analyzed, which were divided into invasive adenocarcinoma group (n=170) and non-invasive adenocarcinoma group (n=53), and the non-invasive adenocarcinoma group was subdivided into minimally invasive adenocarcinoma group (n=31) and preinvasive lesions group (n=22). The malignant probability and imaging characteristics of each group were compared to analyze their predictive ability for the invasive subtypes of early-stage lung adenocarcinoma. The concordance between qualitative diagnostic results of artificial intelligence-assisted diagnosis of the invasive subtypes of early-stage lung adenocarcinoma and postoperative pathology was then analyzed. Results In different invasive subtypes of early-stage lung adenocarcinoma, the mean CT value of pulmonary nodules .001), malignant probability (P<0.001), pleural retraction sign (P<0.001), lobulation (P<0.001), spiculation (P<0.001) were significantly different. At the same time, it was also found that with the increased invasiveness of different invasive subtypes of early-stage lung adenocarcinoma, the proportion of dominant signs of each group gradually increased. On the issue of binary classification, the sensitivity, specificity, and area under the curve (AUC) values of the artificial intelligence-assisted diagnosis system for the qualitative diagnosis of invasive subtypes of early-stage lung adenocarcinoma were 81.76%, 92.45% and 0.871 respectively. On the issue of three classification, the accuracy, recall rate, F1 score, and AUC values of the artificial intelligence-assisted diagnosis system for the qualitative diagnosis of invasive subtypes of early-stage lung adenocarcinoma were 83.86%, 85.03%, 76.46% and 0.879 respectively. Conclusion Artificial intelligence-assisted diagnosis system could predict the invasive subtypes of early-stage lung adenocarcinoma appearing as pulmonary nodules, and has a certain predictive value. With the optimization of algorithms and the improvement of data, it may provide guidance for individualized treatment of patients. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

ECG analysis is an important basis for doctors to diagnose arrhythmia. The judgment of arrhythmia helps patients understand their physical conditions in time and find potential diseases. However, ECG analysis is not only time-consuming and labor-intensive, but also relies on clinical experience. Therefore, the efficiency of ECG analysis has always been limited by the number of doctors and work efficiency. The development of deep learning technology provides a foundation for the development of computer-aided diagnosis systems. In this study, a one-dimensional ECG signal is converted into a two-dimensional gray image, and a GAN-CNN network is used to solve the problem of ECG data imbalance, which can simultaneously realize the recognition of 7 types of arrhythmia and normal heartbeat. The experiment is verified by the MIT-BIH arrhythmia database. The average accuracy rate reaches 99.32%, and the sensitivity and specificity are 99.69% and 98.91%, respectively. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Early diagnosis and treatment of colorectal polyps are crucial for preventing colorectal cancer. This paper proposes a lightweight convolutional neural network for the automatic detection and auxiliary diagnosis of colorectal polyps. Initially, a 53-layer convolutional backbone network is used, incorporating a spatial pyramid pooling module to achieve feature extraction with different receptive field sizes. Subsequently, a feature pyramid network is employed to perform cross-scale fusion of feature maps from the backbone network. A spatial attention module is utilized to enhance the perception of polyp image boundaries and details. Further, a positional pattern attention module is used to automatically mine and integrate key features across different levels of feature maps, achieving rapid, efficient, and accurate automatic detection of colorectal polyps. The proposed model is evaluated on a clinical dataset, achieving an accuracy of 0.9982, recall of 0.9988, F1 score of 0.9984, and mean average precision (mAP) of 0.9953 at an intersection over union (IOU) threshold of 0.5, with a frame rate of 74 frames per second and a parameter count of 9.08 M. Compared to existing mainstream methods, the proposed method is lightweight, has low operating configuration requirements, high detection speed, and high accuracy, making it a feasible technical method and important tool for the early detection and diagnosis of colorectal cancer.

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

To solve the problem of data scarcity and expensive costs in manual labeling of CT images of solid pulmonary nodules, a computer aided diagnosis algorithm for classification between lung tuberculosis and lung adenocarcinoma of solid pulmonary nodules by combining generative adversarial network and ensemble learning was proposed. Firstly, the original CT image dataset was augmented using Wasserstein generative adversarial network with gradient penalty ( WGAN-GP), in order to relieve the problem of overfitting caused by small-scale dataset and class imbalance. Then, feature extraction was performed using convolutional neural network, followed by a dimension reduction procedure using principal component analysis ( PCA) . Finally, deep features concatenated with effective subjective features were classified by an ensemble learning model to give final prediction of the patient. Analysis based on multi-center clinical data indicated that the proposed algorithm has better performance compared to traditional convolutional neural network method. [ABSTRACT FROM AUTHOR]

BACKGROUND: The diagnosis of lumbar disc degeneration is of great significance for the prevention of lumbar disease, and the diagnosis of lumbar disc degeneration mainly relies on the subjective evaluation of the imaging physician, which is likely to misjudge because of insufficient experience. OBJECTIVE: To propose a computer-aided diagnosis technique for classification on the lumbar disc degeneration, and to provide reference for imaging doctors. METHODS: Spearman correlation analysis is used to verify the correlation between magnetic resonance imaging metabolic indices of lumbar intervertebral disc and the Pfirrmann grades of lumbar disc degeneration. Several classifiers for the intelligent diagnosis of lumbar disc degeneration are developed by means of machine learning strategies such as the Softmax regression, the neural network and the support vector machine. RESULTS AND CONCLUSSION: The result of correlation analysis showed that three metabolic indices such as fat fraction (FF) of adjacent upper and lower vertebral bodies of degenerative disc, T2* values were significantly correlated with lumbar disc degeneration. The classification accuracy of the softmax regression, the neural network and the support vector machine respectively was 0.477, 0.515 and 0.523, and kappa’s coefficient of these three diagnostic models was 0.311, 0.300 and 0.330, respectively. The actual analysis indicates that it is feasible to establish a computer-aided intelligent diagnosis of lumbar disc degeneration by using the MRI metabolic indices, showing a promising approach for the diagnosis of lumbar disc degeneration. [ABSTRACT FROM AUTHOR]

Background Glioma is a kind of intracranial space - occupying lesion, which causes heavy disease burden. For glioma patients, early accurate diagnosis and early treatment could effectively prolong the progression free survival. Suspected glioma patients would be firstly examined by MRI for diagnosis. The MRI images were mainly read by radiologists artificially. The diagnosis by different radiologists might be different, and the heavy workload might cause a decline in reading efficiency. In recent years, it is becoming possible to use deep learning technology for medical image recognition and diagnosis. This study used machine learning algorithms related to artificial neural network (ANN) to assist image practitioner in reading the head MRI images of patients with glioma, which may cause labor-saving, and could increase the efficiency of reading images and reduce the different results cause by different personal experiencse. Methods These images were from The Cancer Imaging Archive (TCIA) database. Format of these images was DICOM. And they were from 130 adult glioma cases, a total of 40 036 copies. These images were randomly split as training set (28 025 copies) and test set (12 011 copies). Then in each set, images were split as "tumor image" and "normal image" according to medical experts' annotation. ZFNet model, a type of convolutional neural network, was used to build image recognition and classification model. The reinforcement learning curve was draw to observe the trend of accuracy of training changed with the training steps. Put the test set into the model, the overall classification accuracy of all MRI images, the positive predictive value, sensitivity, specificity and F1-measure of the tumor images were calculated. At the same time, AlexNet was also used to build a same model to compare with the ZFNet model by the prediction indexes of the classification ability of MRI images (the prediction ability of diagnosis of glioma). Results The training accuracy of ZFNet model was 99.7% after 38 757 steps and of the AlexNet model was 98.23% after 37 984 steps. After testing, the image prediction accuracy of all MRI images of ZFNet model was 84.42% (10 140/12 011), the positive predictive value of prediction of "tumor image" was 80.77% (4817/5964), the sensitivity was 86.93% (4817/5541), the specificity was 82.27% (5323/ 6470), and the F1-measure was 83.74%. The above indexes of AlexNet model were 80.74% (9698/12 011), 77.68% (4529/5830), 81.74% (4529/5541), 79.89% (5169/6470) and 79.66%, respectively. The classification performances of ZFNet model were satisfied and were superior to AlexNet model in each dimension. Conclusions ZFNet model has a good prediction performance ability in glioma brain MRI image classification, and it is able to provide a good technical support for establishing a glioma image aided diagnosis model. [ABSTRACT FROM AUTHOR]

Objective The purpose of this study was to evaluate the effect of different reconstruction kernels on deep learning based computer-aided diagnosis (DL-CAD) system detection of benign and malignancy pulmonary nodules at contrast-enhanced CT. Methods Contrast-enhanced CT images were collected from 40 patients with pathologically confirmed with benign (10) and malignant (30) lung nodule between January 2018 and January 2019. Image sets were simulated with different reconstruction kernel (B30f and B70f) at 1-mm thickness. Each nodule was screened using a DL-CAD system (lnferRead Lung CT Research, lnfervision, Beijing, China) and assigned with 0-100% probability of malignancy. Using B30 and B70 reconstruction algorithms, we recorded the probability value of malignancy per nodule and compared with pathological diagnosis. Accuracy in categorizing benign and malignant nodules and the area under the receiver operating characteristic curve (AUC) for different reconstruction kernels were also calculated. Results For reconstruction kernel of B30, diagnostic accuracy was 42.86% for benign and 90.00% for malignant nodules. B70 demonstrated accuracy of 44.44% for benign and 86.67% for malignant nodules. B70 had greater AUC of 0. 704 than B30 (0.473) for predicting all lesions. Conclusion Reconstruction kernel of B70 with the use of DL-CAD system is superior to B30 for differentiating benign from malignant lung nodules. [ABSTRACT FROM AUTHOR]

Aiming at the computer-aided diagnosis of diseased retinal vascular structure, a retinal blood vessel segmentation method of multi-scale convolution kernel U-Net model was proposed. Based on the U-Net model, a multi-scale convolutional neural network structure combining with the Inception module and the maximum index value upsampling method was designed. In the network training stage, operations such as rotation and mirroring were used to expand the data sets, and the CLAHE algorithm was used for image preprocessing. The dual-channel feature map obtained after training was normalized by Softmax. Finally, the normalized result was iteratively optimized by the improved cost loss function, then a complete retinal vessel segmentation model was obtained. Experimental results showed that the proposed method on the DRIVE data set achieved an accuracy of 0. 969 4, a sensitivity of 0. 776 2, and a specificity of 0. 983 5. The proposed method has better segmentation effect and generalization ability than the U-Net model, and shows its competitive results compared with other existing methods. [ABSTRACT FROM AUTHOR]

Copyright of Chinese Medical Equipment Journal is the property of Chinese Medical Equipment Journal Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Chinese Medical Equipment Journal is the property of Chinese Medical Equipment Journal Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Harbin Institute of Technology. Social Sciences Edition / Haerbin Gongye Daxue Xuebao. Shehui Kexue Ban is the property of Harbin Institute of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Background and objective Lung cancer is the cancer with the highest morbidity and mortality at home and abroad at present. Using computed tomography (CT) to screen lung cancer nodules is a huge workload. To test the effect of artificial intelligence in automatic identification of lung cancer by using artificial intelligence to find the lung cancer nodules automatically in the chest CT of 1 mm and 5 mm thick. Methods 5,000 cases of T1 stage lung cancer patients with 1 mm and 5 mm layer thickness were respectively labeled and learned by computer neural network, the algorithm of forming pulmonary nodules was carried out. 500 cases of chest CT in T1 stage lung cancer patients with 1 mm and 5 mm thickness were tested by artificial intelligence formation, and the sensitivity and specificity were compared with artificial reading. Results Using artificial intelligence to read chest CT 500 in 5 mm, the sensitivity was 95.20%, the specificity was 93.20%, and the Kappa value of two times repeated read was 0.926,1. For 1 mm chest CT 500 cases, the sensitivity is 96.40%, the specificity is 95.60%, and the Kappa reads two times is 0.938,6. Compared with 5 doctors, the same CT sets with 1 mm thickness were read. The detection rates of artificial intelligence and artificial reading were similar to those of lung cancer nodules and negative control read films, and there was no significant difference between them. In the comparison of the same CT slices with 5 mm thickness, the number of detection of lung cancer nodules by artificial intelligence is better than that of artificial reading, and the sensitivity is higher, but the number of false messages is increased and the specificity is slightly worse. Conclusion The automatic learning of early lung cancer chest CT images by artificial intelligence can achieve high sensitivity and specificity of early lung cancer identification, and assist doctors in the diagnosis of lung cancer. [ABSTRACT FROM AUTHOR]

Objective: To report the clinical data and pathological data of one case of AA systemic amyloidosis, and analyze the special phenomenon about this case, summarizing the common clinical manifestations and key points for the diagnosis and treatment of amyloidosis. Methods: Retrospective analysis of a case of AA systemic amyloidosis, which presenting as abdominal amyloidoma and multiple systemic dysfunction, about its clinical data and diagnosis and treatment process. Reviewed the literature in combination with her clinical data and diagnosis and treatment process, to clarify the clinical characteristics, key points of diagnosis and treatment. Results:This case report a 62-year-old Chinese woman, who developed symptoms of numbness, anemia, loss of appetite, diarrhea, edema,proteinuria and renal insufficiency one after another since 2012. And she had a history of pulmonary tuberculosis and thoracic tuberculosis. She was found to have severe iron deficiency anemia, nephrotic syndrome, creatinine was 258 μmol/L, globulin increased,rapid blood sedimentation, the ratio of immunoglobulin light chain κ/λ was 1.39, and other immunological tests were all negative during the course of the examination. There was no abnormal proliferating plasma cells were found in bone marrow cytology. Imaging examination revealed an oval large mass in her left lower abdomen with multiple enlarged lymph nodes after the peritoneum.Colonoscopy for the diagnosis of erosive colitis. Combining with her pathological results of abdominal mass, colonic mucosa and renal biopsy, her finally diagnosis was AA systemic amyloidosis secondary to tuberculosis infection. Conclusions: The clinical manifestation of systemic amyloidosis are varied and lack of specificity, therefore, it is often misdiagnosed or missed. In addition to involve multiple system, this case is more special for founding amyloidoma in the patient abdomen, and this rare clinical manifestation is easily misdiagnosed as a tumor and affects treatment. Therefore, tissue biopsy should be performed in time for this type of patient, and pathological and special staining will help early diagnosis of systemic amyloidosis, reduce and delay the occurrence of complications. [ABSTRACT FROM AUTHOR]