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Immuno-score, a prognostic measure for cancer, employed in determining tumor grade and type, is generated by counting the number of Tumour-Infiltrating Lymphocytes (TILs) in CD3 and CD8 stained histopathological tissue samples. Significant stain variations and heterogeneity in lymphocytes' spatial distribution and density make automated counting of TILs' a challenging task.This work addresses the aforementioned challenges by developing a pipeline "Two-Phase Deep Convolutional Neural Network based Lymphocyte Counter (TDC-LC)" to detect lymphocytes in CD3 and CD8 stained histology images. The proposed pipeline sequentially works by removing hard negative examples (artifacts) in the first phase using a custom CNN "LSATM-Net" that exploits the idea of a split, asymmetric transform, and merge. Whereas, in the second phase, instance segmentation is performed to detect and generate a lymphocyte count against the remaining samples. Furthermore, the effectiveness of the proposed pipeline is measured by comparing it with the state-of-the-art single- and two-stage detectors. The inference code is available at GitHub Repository https://github.com/m-mohsin-zafar/tdc-lc.The empirical evaluation on samples from LYSTO dataset shows that the proposed LSTAM-Net can learn variations in the images and precisely remove the hard negative stain artifacts with an F-score of 0.74. The detection analysis shows that the proposed TDC-LC outperforms the existing models in identifying and counting lymphocytes with high Recall (0.87) and F-score (0.89). Moreover, the commendable performance of the proposed TDC-LC in different organs suggests a good generalization.The promising performance of the proposed pipeline suggests that it can serve as an automated system for detecting and counting lymphocytes from patches of tissue samples thereby reducing the burden on pathologists.

In digital pathology, preliminary step for the development of Computer Aided Diagnostics involves defining and labelling an Object of Interest. Contrary to traditional tiresome method of observation and marking of objects under microscope, digital pathology enables the use of annotation tools to capture localization information and morphology for targeted object(s). However, it is quite difficult to capture this information due to high inter and intra class variance. In this paper, we have addressed these two different problems by developing an easy to use and efficient tool "Lymphocyte Annotator" that allows a user to draw bounding box(es) on IHC stained images, and as a result, automatically generates multiple variants of segmentation masks for the targeted object. Lymphocyte Annotator provides multiple options for bounding box selection, with variation in bounding box size and type. The developed tool targets labelling of lymphocytes in histopathological images. This annotator generates five different types of masks by using a) Chan Vese Segmentation Model, b) Morphological Active Contours without Edges Model, c) Morphological Geodesic Active Contours Model, d) Adaptive Thresholding and e) Circular Masks, thus providing user with an opportunity to use a mixture of different types of generated masks. Capturing of lymphocytes' morphology using different models incorporates morphological variance and enables training of robust deep Convolutional Neural Network. The annotations created from this tool are by default exported in PASCAL VOC and JSON format. Lymphocyte annotator is an open-source tool, and can be accessed from source, available at https://github.com/m-mohsin-zafar/lysto_labeltool.

The new emerging COVID-19, declared a pandemic disease, has affected millions of human lives and caused a massive burden on healthcare centers. Therefore, a quick, accurate, and low-cost computer-based tool is required to timely detect and treat COVID-19 patients. In this work, two new deep learning frameworks: Deep Hybrid Learning (DHL) and Deep Boosted Hybrid Learning (DBHL), is proposed for effective COVID-19 detection in X-ray dataset. In the proposed DHL framework, the representation learning ability of the two developed COVID-RENet-1 & 2 models is exploited individually through a machine learning (ML) classifier. In COVID-RENet models, Region and Edge-based operations are carefully applied to learn region homogeneity and extract boundaries features. While in the case of the proposed DBHL framework, COVID-RENet-1 & 2 are fine-tuned using transfer learning on the chest X-rays. Furthermore, deep feature spaces are generated from the penultimate layers of the two models and then concatenated to get a single enriched boosted feature space. A conventional ML classifier exploits the enriched feature space to achieve better COVID-19 detection performance. The proposed COVID-19 detection frameworks are evaluated on radiologist's authenticated chest X-ray data, and their performance is compared with the well-established CNNs. It is observed through experiments that the proposed DBHL framework, which merges the two-deep CNN feature spaces, yields good performance (accuracy: 98.53%, sensitivity: 0.99, F-score: 0.98, and precision: 0.98). Furthermore, a web-based interface is developed, which takes only 5–10s to detect COVID-19 in each unseen chest X-ray image. This web-predictor is expected to help early diagnosis, save precious lives, and thus positively impact society., Graphical abstract Image 1

Background The recent emergence of a highly infectious and contagious respiratory viral disease known as COVID-19 has vastly impacted human lives and overloaded the health care system. Therefore, it is crucial to develop a fast and accurate diagnostic system for the timely identification of COVID-19 infected patients and thus to help control its spread. Methods This work proposes a new deep CNN based technique for COVID-19 classification in X-ray images. In this regard, two novel custom CNN architectures, namely COVID-RENet-1 and COVID-RENet-2, are developed for COVID-19 specific pneumonia analysis. The proposed technique systematically employs Region and Edge-based operations along with convolution operations. The advantage of the proposed idea is validated by performing series of experimentation and comparing results with two baseline CNNs that exploited either a single type of pooling operation or strided convolution down the architecture. Additionally, the discrimination capacity of the proposed technique is assessed by benchmarking it against the state-of-the-art CNNs on radiologist's authenticated chest X-ray dataset. Implementation is available at https://github.com/PRLAB21/Coronavirus-Disease-Analysis-using-Chest-X-Ray-Images . Results The proposed classification technique shows good generalization as compared to existing CNNs by achieving promising MCC (0.96), F-score (0.98) and Accuracy (98%). This suggests that the idea of synergistically using Region and Edge-based operations aid in better exploiting the region homogeneity, textural variations, and region boundary-related information in an image, which helps to capture the pneumonia specific pattern. Conclusions The encouraging results of the proposed classification technique on the test set with high sensitivity (0.98) and precision (0.98) suggest the effectiveness of the proposed technique. Thus, it suggests the potential use of the proposed technique in other X-ray imagery-based infectious disease analysis.

Development of a diagnostic model using deep learning techniques is one of the eminent areas of research in computer aided diagnostic (CAD) systems. CAD systems based on deep learning have been developed to automate the process of mitosis classification. However, mitosis classification usually suffers from class imbalance problem, moreover, high similarity between mitotic and non-mitotic nuclei as well as discrepancy in appearance and shape make classification of mitotic nuclei a challenging area of research. Quality of CAD systems that use medical images for diagnosis, highly depends on how image features are manipulated in order to get better results. This work suggests a new technique of Channel Boosted Convolutional Neural Network (CB-CNN) to classify breast cancer mitotic nuclei. In this technique, feature representation of a CNN is boosted by adding auxiliary feature channels along with original feature space (Red, Green and Blue channels) to increase the generalization of model for heterogeneous and sparse data. In the proposed method, initially (80x80) patches of mitotic and non-mitotic nuclei were extracted from histopathological images by performing histogram based binary thresholding. For the development of a CB-CNN, the potential of auxiliary feature learners was exploited to learn high-level feature representation. In this regard, an additional set of texture and gradient based feature channels were concatenated with original RGB features space of data. This boosted representation was assigned to a custom made deep CNN model. Learning capacity of the proposed CB-CNN is evaluated on TUPAC'16 challenge dataset. Channel Boosting based CNNs (0.53 for CB-CNN, 0.71 for CB-VGG and 0.54 for CB-ResNet) show improved performance in terms of F-score as compared to SVM (F-score: 0.42), and baseline CNN (0.47 for CNN, 0.55 for VGG and 0.44 for ResNet) classifier with and without transfer learning. The improved performance of CB-CNN suggests that the boosting of channel representation improves the generalization of a CNN by making feature space more versatile and flexible, thus making it robust towards heterogeneous data.

Deep Convolutional Neural Network (CNN) is a special type of Neural Networks, which has shown exemplary performance on several competitions related to Computer Vision and Image Processing. Some of the exciting application areas of CNN include Image Classification and Segmentation, Object Detection, Video Processing, Natural Language Processing, and Speech Recognition. The powerful learning ability of deep CNN is primarily due to the use of multiple feature extraction stages that can automatically learn representations from the data. The availability of a large amount of data and improvement in the hardware technology has accelerated the research in CNNs, and recently interesting deep CNN architectures have been reported. Several inspiring ideas to bring advancements in CNNs have been explored, such as the use of different activation and loss functions, parameter optimization, regularization, and architectural innovations. However, the significant improvement in the representational capacity of the deep CNN is achieved through architectural innovations. Notably, the ideas of exploiting spatial and channel information, depth and width of architecture, and multi-path information processing have gained substantial attention. Similarly, the idea of using a block of layers as a structural unit is also gaining popularity. This survey thus focuses on the intrinsic taxonomy present in the recently reported deep CNN architectures and, consequently, classifies the recent innovations in CNN architectures into seven different categories. These seven categories are based on spatial exploitation, depth, multi-path, width, feature-map exploitation, channel boosting, and attention. Additionally, the elementary understanding of CNN components, current challenges, and applications of CNN are also provided., Number of Pages: 70, Number of Figures: 11, Number of Tables: 11. Artif Intell Rev (2020)

This work presents the evaluation of Raman spectroscopy using random forest (RF) for the analysis of dengue fever in the infected human sera. A total of 100 dengue suspected blood samples, collected from Holy Family Hospital, Rawalpindi, Pakistan, have been used in this study. Out of these samples, 45 were dengue-positive based on immunoglobulin M (IgM) capture enzyme-linked immunosorbent assay (ELISA) tests. For highlighting the spectral differences between normal and infected samples, an effective machine learning system is developed that automatically learns the pattern of the shift in spectrum for the dengue compared to normal cases and thus is able to predict the unknown class based on the known example. In this connection, dimensionality reduction has been performed with the principal component analysis (PCA), while RF is used for automatic classification of dengue samples. For the determination of diagnostic capabilities of Raman spectroscopy based on RF, sensitivity, specificity, and accuracy have been calculated in comparison to normally performed IgM capture ELISA. According to the experiment, accuracy of 91%, sensitivity of 91%, and specificity of 91% were achieved for the proposed RF-based model.

This work presents a diagnostic system for the hepatitis C infection using Raman spectroscopy and proximity based classification. The proposed method exploits transformed Raman spectra using the proximity based machine learning technique and is denoted as RS-PCA-Prox. First, Raman spectral data is baseline corrected by subtracting noise and low intensity background. After this, a feature transformation of Raman spectra is adopted, not only to reduce the feature’s dimensionality but also to learn different deviations in Raman shifts. The proposed RS-PCA-Prox shows significant diagnostic power in terms of accuracy, sensitivity, and specificity as 95%, 0.97 and 0.94 in PCA based transformed domain. The comparison of the RS-PCA-Prox with linear and ensemble based classifiers shows that proximity based classification performs better for the discrimination of HCV infected individuals and is able to differentiate the infected individuals from normal ones on the basis of molecular spectral information. Furthermore, it is observed that characteristic spectral changes are due to variation in the intensity of lectin, chitin, lipids, ammonia and viral protein as a consequence of the HCV infection.