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Abstract: Gallbladder polyps (GBPs), especially GBPs ≥5 mm in diameter, are clinically important because they can progress to gallbladder cancer. The known modifiable risk factors for GBP are obesity, metabolic syndrome, and dyslipidemia; however, there is limited evidence regarding specific modifiable risk factors for GBPs ≥5 mm in diameter. Therefore, this study is aimed to investigate the existence of modifiable risk factors affecting the prevalence of GBPs and GBPs ≥5 mm in diameter in a Korean population.A total of 10,119 subjects who visited a single health-screening center at Jeju National University Hospital between January 2009 and December 2019 was included in this study. Binary logistic analyses were performed to identify risk factors affecting the prevalence of GBPs and GBPs ≥5 mm in diameter.The overall prevalence of GBPs and GBPs ≥5 mm in diameter were 9.0% and 4.1%, respectively. Multivariable analysis identified male gender as an independent risk factor affecting the prevalence of GBPs. Moreover, multivariable analysis revealed age and high-density lipoprotein cholesterol levels as independent risk factors for GBPs ≥5 mm in diameter.This study showed that gender was a risk factor affecting the prevalence of GBPs and that age and high-density lipoprotein-cholesterol levels were risk factors for the presence of GBPs ≥5 mm in diameter. High-density lipoprotein cholesterol levels could be a modifiable risk factor affecting the prevalence of large-diameter GBPs., Competing Interests: The authors have no conflicts of interest to disclose., (Copyright © 2021 the Author(s). Published by Wolters Kluwer Health, Inc.)

Colorectal cancer (CRC) is a common digestive system tumor with high morbidity and mortality worldwide. At present, the use of computer-assisted colonoscopy technology to detect polyps is relatively mature, but it still faces some challenges, such as missed or false detection of polyps. Therefore, how to improve the detection rate of polyps more accurately is the key to colonoscopy. To solve this problem, this paper proposes an improved YOLOv5-based cancer polyp detection method for colorectal cancer. The method is designed with a new structure called P-C3 incorporated into the backbone and neck network of the model to enhance the expression of features. In addition, a contextual feature augmentation module was introduced to the bottom of the backbone network to increase the receptive field for multi-scale feature information and to focus on polyp features by coordinate attention mechanism. The experimental results show that compared with some traditional target detection algorithms, the model proposed in this paper has significant advantages for the detection accuracy of polyp, especially in the recall rate, which largely solves the problem of missed detection of polyps. This study will contribute to improve the polyp/adenoma detection rate of endoscopists in the process of colonoscopy, and also has important significance for the development of clinical work. [ABSTRACT FROM AUTHOR]

Colon polyps have become a focal point of research due to their heightened potential to develop into appendiceal cancer, which has the highest mortality rate globally. Although numerous colon polyp segmentation methods have been developed using public polyp datasets, they tend to underperform on private datasets due to inconsistencies in data distribution and the difficulty of fine-tuning without annotations. In this paper, we propose a Self-Adaptive Teacher-Student (SATS) framework to segment colon polyps from unannotated private data by utilizing multiple publicly annotated datasets. The SATS trains multiple teacher networks on public datasets and then generates pseudo-labels on private data to assist in training a student network. To enhance the reliability of the pseudo-labels from the teacher networks, the SATS includes a newly proposed Uncertainty and Distance Fusion (UDFusion) strategy. UDFusion dynamically adjusts the pseudo-label weights based on a novel reconstruction similarity measure, innovatively bridging the gap between private and public data distributions. To ensure accurate identification and segmentation of colon polyps, the SATS also incorporates a Granular Attention Network (GANet) architecture for both teacher and student networks. GANet first identifies polyps roughly from a global perspective by encoding long-range anatomical dependencies and then refines this identification to remove false-positive areas through multi-scale background-foreground attention. The SATS framework was validated using three public datasets and one private dataset, achieving 76.30% on IoU, 86.00% on Recall, and 7.01 pixels on HD. These results outperform the existing five methods, indicating the effectiveness of this approach for colon polyp segmentation. [ABSTRACT FROM AUTHOR]

The accurate segmentation of colorectal polyps is of great significance for the diagnosis and treatment of colorectal cancer. However, the segmentation of colorectal polyps faces complex problems such as low contrast in the peripheral region of salient images, blurred borders, and diverse shapes. In addition, the number of traditional UNet network parameters is large and the segmentation effect is average. To overcome these problems, an innovative nonlinear activation-free uncertainty contextual attention network is proposed in this paper. Based on the UNet network, an encoder and a decoder are added to predict the saliency map of each module in the bottom-up flow and pass it to the next module. We use Res2Net as the backbone network to extract image features, enhance image features through simple parallel axial channel attention, and obtain high-level features with global semantics and low-level features with edge details. At the same time, a nonlinear n on-activation network is introduced, which can reduce the complexity between blocks, thereby further enhancing image feature extraction. This work conducted experiments on five commonly used polyp segmentation datasets, and the experimental evaluation metrics from the mean intersection over union, mean Dice coefficient, and mean absolute error were all improved, which can show that our method has certain advantages over existing methods in terms of segmentation performance and generalization performance. [ABSTRACT FROM AUTHOR]

Background: Gallbladder polyps are increasingly being identified due to the widespread use of abdominal ultrasound imaging. They are concerning lesions due to their potential malignant risk. It is hoped that managing them correctly will play a role in improving poor survival rates of gallbladder cancer. Awareness of these lesions is lacking. Management continues to be guided by expert opinion and observational studies and a number of consensus statements exist. Objective: This paper reviews and summarises the current literature and provides an approach for general practitioners based on the available guidance. Discussion: Although minor variation exists between consensus statements, the risk of malignancy for gallbladder polyps is still largely dictated by size, with those ≤5 mm generally considered to pose little risk and not requiring follow-up, whereas those ≥10 mm considered at greater risk and requiring referral for cholecystectomy. [ABSTRACT FROM AUTHOR]

Gallbladder polyp is a common disease of gallbladder, the incidence of gallbladder polyp in China is about 5%~10%, and the trend is increasing year by year. The patients with gallbladder polyps had no obvious clinical symptoms, which was more than that found by ultrasonography during physical examination. At present, the diameter of gallbladder polyps>10 mm is still used by clinicians as the main surgical indication for cholecystectomy. According to the data, about 80% to 90% of gallbladder polyps are cholesterol type polyps and benign gallbladder polyps. For these patients whose gallbladder is removed due to benign gallbladder polyps, we consider that we can continue to observe or retain the gallbladder, without having to bear the adverse consequences that may be caused by gallbladder removal. Based on the literature analysis at home and abroad, this paper discusses the surgical treatment of gallbladder polyps and the results of postoperative pathological diagnosis, and reminds the majority of clinicians to be careful when removing gallbladder polyps.

Rationale and Objectives: To investigate the safety and efficacy of ultrasound-guided radiofrequency ablation (RFA) in the treatment of gallbladder polyps., Materials and Methods: A retrospective analysis was conducted on the medical records of 296 patients diagnosed with gallbladder polyps. The study observed the changes in lesions post-procedure within the ablation group, and compared whether there was a difference in the gallbladder contraction rate in patients before and after ablation. It also compared the liver function indicators before and after surgery, some indicators during the periprocedural period, and the incidence of complications in two groups of patients., Results: In the ablation group, all lesions (84/84) were completely ablated, and the absorption effect of the ablation lesions was good after the ablation. No significant differences were observed in the gallbladder contraction rate before ablation compared to 1 month and 3 months post-ablation (p > 0.05). After the operation, statistically significant differences were observed in ALT and TP between the two groups (all p < 0.05). Significant differences were observed between the two groups in terms of hospital stay, procedural time, postprocedural mobilization time, postprocedural exhaust time, postprocedural eating time, and VAS score on postprocedural day 3 (all p < 0.05). There was no significant difference in the incidence of complications between the two groups (x 2 =0.477，p = 0.490)., Conclusion: Our findings demonstrate that ultrasound-guided RFA is a safe, effective, and feasible treatment for gallbladder polyps, as it not only effectively eliminates the polyps but also preserves the physiological functions of the gallbladder., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.)

Colorectal polyp is an important early manifestation of colorectal cancer, which is significant for the prevention of colorectal cancer. Despite timely detection and manual intervention of colorectal polyps can reduce their chances of becoming cancerous, most existing methods ignore the uncertainties and location problems of polyps, causing a degradation in detection performance. To address these problems, in this paper, we propose a novel colorectal image analysismethod for polyp diagnosis via PAM-Net. Specifically, a parallel attention module is designed to enhance the analysis of colorectal polyp images for improving the certainties of polyps. In addition, ourmethod introduces the GWD loss to enhance the accuracy of polyp diagnosis from the perspective of polyp location. Extensive experimental results demonstrate the e [ABSTRACT FROM AUTHOR]

In a colonoscopy, accurate computer-aided polyp detection and segmentation can help endoscopists to remove abnormal tissue. This reduces the chance of polyps developing into cancer, which is of great importance. In this paper, we propose a neural network (parallel residual atrous pyramid network or PRAPNet) based on a parallel residual atrous pyramid module for the segmentation of intestinal polyp detection. We made full use of the global contextual information of the different regions by the proposed parallel residual atrous pyramid module. The experimental results showed that our proposed global prior module could effectively achieve better segmentation results in the intestinal polyp segmentation task compared with the previously published results. The mean intersection over union and dice coefficient of the model in the Kvasir-SEG dataset were 90.4% and 94.2%, respectively. The experimental results outperformed the scores achieved by the seven classical segmentation network models (U-Net, U-Net++, ResUNet++, praNet, CaraNet, SFFormer-L, TransFuse-L). [ABSTRACT FROM AUTHOR]

Automatic segmentation of renal tumours during renal cell carcinoma surgery can help doctors accurately locate the tumour region, protect the tissues and organs around the kidneys, enhance surgical efficiency, and reduce the possibility of leakage and misdiagnosis. However, since general polyp endoscopic image segmentation models have many problems when facing the task of renal tumour segmentation, there needs to be more research on the segmentation of endoscopic images of renal tumours. This paper proposes a multi‐scale dynamic segmentation kernel network for endoscopic image segmentation of kidney tumours. First, a spatial receptive field module is proposed to augment the feature information and improve the performance of the whole network. Second, an enhanced cross‐attention module is offered to attenuate the effect of a high‐similarity segmentation background. Finally, a multi‐scale dynamic segmentation kernel module is introduced to gradually refine the segmentation results from small to large sizes to obtain more accurate tumour boundaries. Extensive experiments on the established kidney tumour endoscopic dataset and publicly available endoscopic datasets show that this method exhibits enhanced performance and generalization capabilities compared to existing techniques. On this renal tumour dataset, MDSK‐Net achieved excellent results of 94.1% and 90.1% on mDice and mIoU. [ABSTRACT FROM AUTHOR]

Colon polyps represent a common gastrointestinal form. In order to effectively treat and prevent complications arising from colon polyps, colon polypectomy has become a commonly used therapeutic approach. Accurately segmenting polyps from colonoscopy images can provide valuable information for early diagnosis and treatment. Due to challenges posed by illumination and contrast variations, noise and artifacts, as well as variations in polyp size and blurred boundaries in polyp images, the robustness of segmentation algorithms is a significant concern. To address these issues, this paper proposes a Double Loss Guided Residual Attention and Feature Enhancement Network (DLGRAFE-Net) for polyp segmentation. Firstly, a newly designed Semantic and Spatial Information Aggregation (SSIA) module is used to extract and fuse edge information from low-level feature graphs and semantic information from high-level feature graphs, generating local loss-guided training for the segmentation network. Secondly, newly designed Deep Supervision Feature Fusion (DSFF) modules are utilized to fuse local loss feature graphs with multi-level features from the encoder, addressing the negative impact of background imbalance caused by varying polyp sizes. Finally, Efficient Feature Extraction (EFE) decoding modules are used to extract spatial information at different scales, establishing longer-distance spatial channel dependencies to enhance the overall network performance. Extensive experiments conducted on the CVC-ClinicDB and Kvasir-SEG datasets demonstrate that the proposed network outperforms all mainstream networks and state-of-the-art networks, exhibiting superior performance and stronger generalization capabilities. [ABSTRACT FROM AUTHOR]

Purpose: To evaluate the impact of endocervical and decidual polypectomy on obstetrical outcomes of pregnant women. Methods: MEDLINE, Scopus, ClinicalTrials.gov, Scielo, EMBASE, Cochrane Library at the CENTRAL Register of Controlled Trials, and LILACS were searched from inception to April 2021. No language or geographical restrictions were applied. Inclusion criteria regarded observational studies concerning pregnant women with a cervical lesion who underwent cervical polypectomy. Co-primary outcomes were incidence of late pregnancy loss and preterm birth in women with endocervical or decidual polypectomy as well as polypectomy versus expectant management. Random effect meta-analyses to calculate risk ratio (RR) with 95% confidence interval (CI) were performed. Quality assessment of included papers was performed using Newcastle–Ottawa Scale criteria. Results: Three studies, with data provided for 3097 women, were included in quantitative analysis, with comparisons between endocervical and decidual polyps extracted from two studies and 156 patients. After a first trimester endocervical or decidual polypectomy, no significant differences were found for late pregnancy losses (RR 0.29 [95% CI 0.05, 1.80], I2 = 11%). Risk for preterm birth was significantly higher for decidual polyps' removal (RR 6.13 [95% CI 2.57, 14.59], I2 = 0%). One paper compared cervical polypectomy vs expectant management, with increased incidence of late pregnancy loss (4/142 vs 5/2799; p < 0.001) and preterm birth (19/142 vs 115/2799; p < 0.001) in women subjected to polypectomy. Conclusions: Evidence regarding the removal of cervical polyps in pregnancy is extremely limited. However, the removal of either decidual or endocervical polyps seems associated with increased risk of pregnancy loss and preterm birth, with increased preterm birth risk following endocervical rather than decidual polypectomy. [ABSTRACT FROM AUTHOR]

The similar shape and texture of colonic polyps and normal mucosal tissues lead to low accuracy of medical image segmentation algorithms. To solve these problems, we proposed a polyp image segmentation algorithm based on deep learning technology, which combines a HarDNet module, attention module, and multi-scale coding module with the U-Net network as the basic framework, including two stages of coding and decoding. In the encoder stage, HarDNet68 is used as the main backbone network to extract features using four null space convolutional pooling pyramids while improving the inference speed and computational efficiency; the attention mechanism module is added to the encoding and decoding network; then the model can learn the global and local feature information of the polyp image, thus having the ability to process information in both spatial and channel dimensions, to solve the problem of information loss in the encoding stage of the network and improving the performance of the segmentation network. Through comparative analysis with other algorithms, we can find that the network of this paper has a certain degree of improvement in segmentation accuracy and operation speed, which can effectively assist physicians in removing abnormal colorectal tissues and thus reduce the probability of polyp cancer, and improve the survival rate and quality of life of patients. Also, it has good generalization ability, which can provide technical support and prevention for colon cancer. [ABSTRACT FROM AUTHOR]

Polyp detection is a challenging task in the diagnosis of Colorectal Cancer (CRC), and it demands clinical expertise due to the diverse nature of polyps. The recent years have witnessed the development of automated polyp detection systems to assist the experts in early diagnosis, considerably reducing the time consumption and diagnostic errors. In automated CRC diagnosis, polyp segmentation is an important step which is carried out with deep learning segmentation models. Recently, Vision Transformers (ViT) are slowly replacing these models due to their ability to capture long range dependencies among image patches. However, the existing ViTs for polyp do not harness the inherent self-attention abilities and incorporate complex attention mechanisms. This paper presents Polyp-Vision Transformer (Polyp-ViT), a novel Transformer model based on the conventional Transformer architecture, which is enhanced with adaptive mechanisms for feature extraction and positional embedding. Polyp-ViT is tested on the Kvasir-seg and CVC-Clinic DB Datasets achieving segmentation accuracies of 0.9891 ± 0.01 and 0.9875 ± 0.71 respectively, outperforming state-of-the-art models. Polyp-ViT is a prospective tool for polyp segmentation which can be adapted to other medical image segmentation tasks as well due to its ability to generalize well., (© 2024. The Author(s).)

Introduction: Although the incidence and mortality rates of colorectal cancer exhibit significant variability, it remains one of the most prevalent cancers worldwide. Endeavors to prevent colorectal cancer development focus on detecting precursor lesions during colonoscopy. The diagnosis of endoscopically resected polyps relies on hematoxylin and eosin staining examination. For challenging cases like adenomatous polyps with epithelial misplacement, additional diagnostic methods could prove beneficial. Methods: This paper aims to underscore stromal changes observed in malignant polyps and polyps with pseudoinvasion, leveraging two-photon excitation microscopy (TPEM), a technique extensively employed in the medical field in recent years. Results and discussions: Both the subjective and quantitative analysis of TPEM images revealed distinct distributions and densities of collagen at the invasion front in malignant polyps compared to areas of pseudoinvasion. TPEM holds potential in discerning true invasion in malignant polyps from pseudoinvasion, offering enhanced visualization of local stromal changes. [ABSTRACT FROM AUTHOR]

Automatically segmenting polyps from colonoscopy videos is crucial for developing computer-assisted diagnostic systems for colorectal cancer. Existing automatic polyp segmentation methods often struggle to fulfill the real-time demands of clinical applications due to their substantial parameter count and computational load, especially those based on Transformer architectures. To tackle these challenges, a novel lightweight long-range context fusion network, named LightCF-Net, is proposed in this paper. This network attempts to model long-range spatial dependencies while maintaining real-time performance, to better distinguish polyps from background noise and thus improve segmentation accuracy. A novel Fusion Attention Encoder (FAEncoder) is designed in the proposed network, which integrates Large Kernel Attention (LKA) and channel attention mechanisms to extract deep representational features of polyps and unearth long-range dependencies. Furthermore, a newly designed Visual Attention Mamba module (VAM) is added to the skip connections, modeling long-range context dependencies in the encoder-extracted features and reducing background noise interference through the attention mechanism. Finally, a Pyramid Split Attention module (PSA) is used in the bottleneck layer to extract richer multi-scale contextual features. The proposed method was thoroughly evaluated on four renowned polyp segmentation datasets: Kvasir-SEG, CVC-ClinicDB, BKAI-IGH, and ETIS. Experimental findings demonstrate that the proposed method delivers higher segmentation accuracy in less time, consistently outperforming the most advanced lightweight polyp segmentation networks. [ABSTRACT FROM AUTHOR]

Deep learning has achieved immense success in computer vision and has the potential to help physicians analyze visual content for disease and other abnormalities. However, the current state of deep learning is very much a black box, making medical professionals skeptical about integrating these methods into clinical practice. Several methods have been proposed to shed some light on these black boxes, but there is no consensus on the opinion of medical doctors that will consume these explanations. This paper presents a study asking medical professionals about their opinion of current state-of-the-art explainable artificial intelligence methods when applied to a gastrointestinal disease detection use case. We compare two different categories of explanation methods, intrinsic and extrinsic, and gauge their opinion of the current value of these explanations. The results indicate that intrinsic explanations are preferred and that physicians see value in the explanations. Based on the feedback collected in our study, future explanations of medical deep neural networks can be tailored to the needs and expectations of doctors. Hopefully, this will contribute to solving the issue of black box medical systems and lead to successful implementation of this powerful technology in the clinic. [ABSTRACT FROM AUTHOR]

Computer-assisted systems are becoming broadly used in medicine. In endoscopy, most research focuses on the automatic detection of polyps or other pathologies, but localization and navigation of the endoscope are completely performed manually by physicians. To broaden this research and bring spatial Artificial Intelligence to endoscopies, data from complete procedures is needed. This paper introduces the Endomapper dataset, the first collection of complete endoscopy sequences acquired during regular medical practice, making secondary use of medical data. Its main purpose is to facilitate the development and evaluation of Visual Simultaneous Localization and Mapping (VSLAM) methods in real endoscopy data. The dataset contains more than 24 hours of video. It is the first endoscopic dataset that includes endoscope calibration as well as the original calibration videos. Meta-data and annotations associated with the dataset vary from the anatomical landmarks, procedure labeling, segmentations, reconstructions, simulated sequences with ground truth and same patient procedures. The software used in this paper is publicly available. [ABSTRACT FROM AUTHOR]

Sarcomas are rare lesions and encompass a wide variety of entities, depending on their nature. In recent years new entities have been described and new knowledge, especially that provided by molecular studies, has been increasing. This makes it very difficult to be updated with all the described entities, since only some of the centers have the desired ancillary studies for the correct diagnosis. Some lesions are extremely rare and may appear once or twice during the lifetime of a general pathologist. When we refer to sarcomas of the gastrointestinal tract, the gastrointestinal stromal tumor (GIST) is the most well-known lesion that the pathologist will most frequently find in daily practice. This paper aims to comprehensively review the sarcomas associated with the gastrointestinal tract, emphasizing histopathology and going beyond GIST. This review highlights the histopathology of rare types of sarcomas so it may increase awareness of common and rare lesions, prompting an easy and effective diagnosis. [ABSTRACT FROM AUTHOR]

This paper presents a novel supervised convolutional neural network architecture, "DUCK-Net", capable of effectively learning and generalizing from small amounts of medical images to perform accurate segmentation tasks. Our model utilizes an encoder-decoder structure with a residual downsampling mechanism and a custom convolutional block to capture and process image information at multiple resolutions in the encoder segment. We employ data augmentation techniques to enrich the training set, thus increasing our model's performance. While our architecture is versatile and applicable to various segmentation tasks, in this study, we demonstrate its capabilities specifically for polyp segmentation in colonoscopy images. We evaluate the performance of our method on several popular benchmark datasets for polyp segmentation, Kvasir-SEG, CVC-ClinicDB, CVC-ColonDB, and ETIS-LARIBPOLYPDB showing that it achieves state-of-the-art results in terms of mean Dice coefficient, Jaccard index, Precision, Recall, and Accuracy. Our approach demonstrates strong generalization capabilities, achieving excellent performance even with limited training data., (© 2023. The Author(s).)

Background/Aims Although the 3-second rule has been shown in our previous study to improve high-confidence (HC) assessment in optical diagnosis, the process remains not well-established. Additionally, the impact of recent advances in artificial intelligence and its interaction with endoscopists remains uncertain. This study aimed to evaluate the effectiveness of a new computer-aided diagnosis (CADx) system, combined with the 3-second rule, in the optical diagnosis of colorectal polyps among endoscopists with varying levels of experience. Methods This multicenter ex vivo study involved 35 endoscopists, each of whom evaluated 100 polyps (51 adenomas, 39 hyperplastic polyps, 10 sessile serrated lesions) using narrow-band imaging (NBI) video clips via an online test platform. The evaluations were conducted in two stages: independent diagnosis and diagnosis with CADx assistance. HC was defined by the 3-second rule established in a previous study. Performance metrics included HC accuracy, HC rate, and adherence to the Preservation and Incorporation of Valuable Endoscopic Innovations (PIVI) and Simple Optical Diagnosis Accuracy (SODA) thresholds. Results The stand-alone performance of CADx demonstrated accuracy, sensitivity, specificity, and positive and negative predictive values of 90.0%, 95.1%, 82.1%, 89.2%, and 91.4%, respectively. The CADx-assisted phase, using the 3-second rule, significantly improved both HC accuracy (78.3% vs. 89.8%; p<0.001) and HC rates (64.2% vs. 75.4%; p<0.001) across all groups. Notably, novice endoscopists exhibited a substantial increase in accuracy with CADx assistance (74.1% vs. 88.8%; p<0.0001) (Table). The CADx-assisted performance for HC diagnoses nearly achieved the SODA standards and PIVI thresholds when combined with the use of the 3-second rule (Figure). Conclusion The new CADx system, when used in conjunction with the 3-second rule, significantly enhances the optical diagnosis of colorectal polyps, particularly among less experienced endoscopists. This combined approach could be valuable for cost-effective management strategies for colorectal polyps. [ABSTRACT FROM AUTHOR]

The U-Net network has its own powerful capabilities in medical image segmentation tasks, yet still it is a challenging task to make U-Net accurately segment the infected lesions of COVID-19 CT images because these lesion areas are usually irregular in shape, various in size, and blurry in boundaries. In this paper, a novel multiscale U-shaped network based on U-Net for accurate segmentation of lesion regions in COVID-19 CT images is proposed. First, we generate two auxiliary scale features(fi 0.5, fi 1.5) based on the main scale feature (fi 1.0) through zoom strategy. Secondly, we design the Scale Integration Module (SIM), which is capable of filtering and aggregating scale-specific features and can fully exploit multi-scale semantic information. Thirdly, the hierarchical mixed module (HMM) has successfully substituted for the down-up aggregation process of the U-Net network, which further enhances the mixed scale features. On the dataset COVID-19-CT829, compared with the recent COVID-19 segmentation model, hiformer, the Dice, Sen and F-measure of our network have increased by 2.24%, 2.83%, 3.14%, respectively; on the dataset COVID-19-CT100, the Dice, Sen and F-measure of our network have increased by 2.91%, 3.72%, 2.42%, respectively. Moreover, we have validated the generalizability and portability of our network on other medical datasets (Polyp segmentation dataset: CVC-612 and kvasir), and our network has also achieved superior results of COVID-19 CT image segmentation. [ABSTRACT FROM AUTHOR]

Virtual colonoscopy plays an important role in polyp detection of colorectal cancer. Noise in the colon data acquisition process can result in topological errors during surface reconstruction. Topological denoising can be employed to remove these errors on surfaces for subsequent geometry processing, such as surface simplification and parameterization. Many methods have been proposed for this task. However, many existing methods suffer from failure in computation of all the non-trivial loops, due to high genus or complex topological structures. In this paper, we propose a novel robust topological denoising method for surfaces based on homotopy theory. The proposed method was evaluated on two datasets of colon meshes. We compared our method with the State-of-the-Art persistent-homology-based method. Our method can successfully compute the loops on all colon data for topological denoising, whereas the persistent homology method fails on some colon data. Moreover, our method detects all loops with shorter lengths than those detected by the persistent homology method. Our experimental results show that the proposed method is effective and robust in topological denoising, and that it has the potential for practical application to virtual colonoscopy. [ABSTRACT FROM AUTHOR]

Colorectal cancer typically affects the gastrointestinal tract within the human body. Colonoscopy is one of the most accurate methods of detecting cancer. The current system facilitates the identification of cancer by computer-assisted diagnosis (CADx) systems with a limited number of deep learning methods. It does not imply the depiction of mixed datasets for the functioning of the system. The proposed system, called ColoRectalCADx, is supported by deep learning (DL) models suitable for cancer research. The CADx system comprises five stages: convolutional neural networks (CNN), support vector machine (SVM), long short-term memory (LSTM), visual explanation such as gradient-weighted class activation mapping (Grad-CAM), and semantic segmentation phases. Here, the key components of the CADx system are equipped with 9 individual and 12 integrated CNNs, implying that the system consists mainly of investigational experiments with a total of 21 CNNs. In the subsequent phase, the CADx has a combination of CNNs of concatenated transfer learning functions associated with the machine SVM classification. Additional classification is applied to ensure effective transfer of results from CNN to LSTM. The system is mainly made up of a combination of CVC Clinic DB, Kvasir2, and Hyper Kvasir input as a mixed dataset. After CNN and LSTM, in advanced stage, malignancies are detected by using a better polyp recognition technique with Grad-CAM and semantic segmentation using U-Net. CADx results have been stored on Google Cloud for record retention. In these experiments, among all the CNNs, the individual CNN DenseNet-201 (87.1% training and 84.7% testing accuracies) and the integrated CNN ADaDR-22 (84.61% training and 82.17% testing accuracies) were the most efficient for cancer detection with the CNN+LSTM model. ColoRectalCADx accurately identifies cancer through individual CNN DesnseNet-201 and integrated CNN ADaDR-22. In Grad-CAM's visual explanations, CNN DenseNet-201 displays precise visualization of polyps, and CNN U-Net provides precise malignant polyps., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Akella S. Narasimha Raju et al.)

Gastric cancer is a deadly disease and gastric polyps are at high risk of becoming cancerous. Therefore, the timely detection of gastric polyp is of great importance which can reduce the incidence of gastric cancer effectively. At present, the object detection method based on deep learning is widely used in medical images. However, as the contrast between the background and the polyps is not strong in gastroscopic image, it is difficult to distinguish various sizes of polyps from the background. In this paper, to improve the detection performance metrics of endoscopic gastric polyps, we propose an improved attentional feature fusion module. First, in order to enhance the contrast between the background and the polyps, we propose an attention module that enables the network to make full use of the target location information, it can suppress the interference of the background information and highlight the effective features. Therefore, on the basis of accurate positioning, it can focus on detecting whether the current location is the gastric polyp or background. Then, it is combined with our feature fusion module to form a new attentional feature fusion model that can mitigate the effects caused by semantic differences in the processing of feature fusion, using multi-scale fusion information to obtain more accurate attention weights and improve the detection performance of polyps of different sizes. In this work, we conduct experiments on our own dataset of gastric polyps. Experimental results show that the proposed attentional feature fusion module is better than the common feature fusion module and can improve the situation where polyps are missed or misdetected. [ABSTRACT FROM AUTHOR]

Chronic rhinosinusitis with nasal polyps (CRSwNP) has long been considered a benign, chronic inflammatory, and hyperplastic disease. Recent studies have shown that autoimmune-related mechanisms are involved in the pathology of nasal polyps. Activated plasma cells, eosinophils, basophils, innate type 2 lymphocytes, mast cells, and proinflammatory cytokine in polyp tissue indicate the mobilization of innate and adaptive immune pathways during polyp formation. The discovery of a series of autoantibodies further supports the autoimmune nature of nasal polyps. Local homeostasis dysregulation, infection, and chronic inflammation may trigger autoimmunity through several mechanisms, including autoantigens overproduction, microbial translocation, molecular mimicry, superantigens, activation or inhibition of receptors, bystander activation, dysregulation of Toll-Like Receptors (TLRs), epitope spreading, autoantigens complementarity. In this paper, we elaborated on the microbiome-mediated mechanism, abnormal host immunity, and genetic changes to update the role of autoimmunity in the pathogenesis of chronic rhinosinusitis with nasal polyps. [ABSTRACT FROM AUTHOR]

Background: Colorectal cancer is a leading cause of cancer-related deaths worldwide. The best method to prevent CRC is a colonoscopy. However, not all colon polyps have the risk of becoming cancerous. Therefore, polyps are classified using different classification systems. After the classification, further treatment and procedures are based on the classification of the polyp. Nevertheless, classification is not easy. Therefore, we suggest two novel automated classifications system assisting gastroenterologists in classifying polyps based on the NICE and Paris classification. Methods: We build two classification systems. One is classifying polyps based on their shape (Paris). The other classifies polyps based on their texture and surface patterns (NICE). A two-step process for the Paris classification is introduced: First, detecting and cropping the polyp on the image, and secondly, classifying the polyp based on the cropped area with a transformer network. For the NICE classification, we design a few-shot learning algorithm based on the Deep Metric Learning approach. The algorithm creates an embedding space for polyps, which allows classification from a few examples to account for the data scarcity of NICE annotated images in our database. Results: For the Paris classification, we achieve an accuracy of 89.35 %, surpassing all papers in the literature and establishing a new state-of-the-art and baseline accuracy for other publications on a public data set. For the NICE classification, we achieve a competitive accuracy of 81.13 % and demonstrate thereby the viability of the few-shot learning paradigm in polyp classification in data-scarce environments. Additionally, we show different ablations of the algorithms. Finally, we further elaborate on the explainability of the system by showing heat maps of the neural network explaining neural activations. Conclusion: Overall we introduce two polyp classification systems to assist gastroenterologists. We achieve state-of-the-art performance in the Paris classification and demonstrate the viability of the few-shot learning paradigm in the NICE classification, addressing the prevalent data scarcity issues faced in medical machine learning. [ABSTRACT FROM AUTHOR]

Automatic segmentation of polyps during colonoscopy can help doctors accurately find the polyp area and remove abnormal tissues in time to reduce the possibility of polyps transforming into cancer. However, the current polyp segmentation research still has the following problems: blurry polyp boundaries, multi-scale adaptability of polyps, and close resemblances between polyps and nearby normal tissues. To tackle these issues, this paper proposes a dual boundary-guided attention exploration network (DBE-Net) for polyp segmentation. Firstly, we propose a dual boundary-guided attention exploration module to solve the boundary-blurring problem. This module uses a coarse-to-fine strategy to progressively approximate the real polyp boundary. Secondly, a multi-scale context aggregation enhancement module is introduced to accommodate the multi-scale variation of polyps. Finally, we propose a low-level detail enhancement module, which can extract more low-level details and promote the performance of the overall network. Extensive experiments on five polyp segmentation benchmark datasets show that our method achieves superior performance and stronger generalization ability than state-of-the-art methods. Especially for CVC-ColonDB and ETIS, two challenging datasets among the five datasets, our method achieves excellent results of 82.4% and 80.6% in terms of mDice (mean dice similarity coefficient) and improves by 5.1% and 5.9% compared to the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Small bowel polyps exhibit variations related to color, shape, morphology, texture, and size, as well as to the presence of artifacts, irregular polyp borders, and the low illumination condition inside the gastrointestinal GI tract. Recently, researchers developed many highly accurate polyp detection models based on one-stage or two-stage object detector algorithms for wireless capsule endoscopy (WCE) and colonoscopy images. However, their implementation requires a high computational power and memory resources, thus sacrificing speed for an improvement in precision. Although the single-shot multibox detector (SSD) proves its effectiveness in many medical imaging applications, its weak detection ability for small polyp regions persists due to the lack of information complementary between features of low- and high-level layers. The aim is to consecutively reuse feature maps between layers of the original SSD network. In this paper, we propose an innovative SSD model based on a redesigned version of a dense convolutional network (DenseNet) which emphasizes multiscale pyramidal feature maps interdependence called DC-SSDNet (densely connected single-shot multibox detector). The original backbone network VGG-16 of the SSD is replaced with a modified version of DenseNet. The DenseNet-46 front stem is improved to extract highly typical characteristics and contextual information, which improves the model's feature extraction ability. The DC-SSDNet architecture compresses unnecessary convolution layers of each dense block to reduce the CNN model complexity. Experimental results showed a remarkable improvement in the proposed DC-SSDNet to detect small polyp regions achieving an mAP of 93.96%, F1-score of 90.7%, and requiring less computational time. [ABSTRACT FROM AUTHOR]

Polyps in the colon are widely known cancer precursors identified by colonoscopy. Whilst most polyps are benign, the polyp's number, size and surface structure are linked to the risk of colon cancer. Several methods have been developed to automate polyp detection and segmentation. However, the main issue is that they are not tested rigorously on a large multicentre purpose-built dataset, one reason being the lack of a comprehensive public dataset. As a result, the developed methods may not generalise to different population datasets. To this extent, we have curated a dataset from six unique centres incorporating more than 300 patients. The dataset includes both single frame and sequence data with 3762 annotated polyp labels with precise delineation of polyp boundaries verified by six senior gastroenterologists. To our knowledge, this is the most comprehensive detection and pixel-level segmentation dataset (referred to as PolypGen) curated by a team of computational scientists and expert gastroenterologists. The paper provides insight into data construction and annotation strategies, quality assurance, and technical validation. [ABSTRACT FROM AUTHOR]

Recently, many salient object detection methods have utilized edge contours to constrain the solution space. This approach aims to reduce the omission of salient features and minimize the inclusion of non-salient features. To further leverage the potential of edge-related information, this paper proposes a Redundant and Missing Feature Decoupling Network (RMFDNet). RMFDNet primarily consists of a segment decoder, a complement decoder, a removal decoder, and a recurrent repair encoder. The complement and removal decoders are designed to directly predict the missing and redundant features within the segmentation features. These predicted features are then processed by the recurrent repair encoder to refine the segmentation features. Experimental results on multiple Red–Green–Blue (RGB) and Red–Green–Blue-Depth (RGB-D) benchmark datasets, as well as polyp segmentation datasets, demonstrate that RMFDNet significantly outperforms previous state-of-the-art methods across various evaluation metrics. The efficiency, robustness, and generalization capability of RMFDNet are thoroughly analyzed through a carefully designed ablation study. The code will be made available upon paper acceptance. • We propose RMFDNet to refine segmentation by mitigating missing/excessive features. • A repair encoder leverages predicted features for accurate saliency prediction. • A plug-in strategy adds complement/removal and repair modules to SOD networks. [ABSTRACT FROM AUTHOR]

Precise identification of colon polyps, which are the principal precursors of colorectal cancer, is crucial for accurate diagnosis and treatment. Current segmentation methods possess unique strengths and limitations, given the colon polyp's varying size, shape, color, and unclear edge with surrounding mucosa. These challenges result in the absence of a universally effective segmentation method. Addressing the aforementioned issues, this paper proposes a novel shape-aware and feature enhancement network (SAFE-Net) for polyp segmentation. SAFE-Net contains three innovative modules: the shape-aware module (SAM), the feature enhancement module (FEM), and the multi-modality attention module (MMAM). SAM leverages the disparities in low-level features within the backbone network to effectively filter out noise from colonoscopy image, thereby extracting distinct detailed features, including shape, texture, and edge, ultimately enhancing the precision of polyp segmentation. FEM enhances and enriches the semantic information of the backbone network's features, enables the capture of polyps varying in shape and size, and selects and refines relevant features. MMAM utilizes high-level feature prediction as a guide map to achieve attention on the background, foreground, and boundary, making the network focus more on suspicious and complex polyp regions. This paper conducts experiments on five colonoscopy image datasets: Kvasir-SEG, CVC-ClinicDB, CVC-ColonDB, CVC-T, and ETIS. Comparative analyses are conducted between the proposed model and existing models. The experimental results confirm that the proposed model exhibits superior segmentation capabilities and provides a reliable foundation for subsequent diagnostic procedures. • A novel SAFE-Net for polyp segmentation is proposed. • SAM use feature disparities to discriminate polyp from background, mitigating noise and extracting precise features. • FEM boost polyp segmentation accuracy for various sizes and shapes, enabling selective feature augmentation. • MMAM utilize hierarchical feature supplementation and prediction refinement to bolster the network's discriminative capability. [Display omitted] [ABSTRACT FROM AUTHOR]