Abstract

Deep learning assisted diagnosis for assessing the severity of various respiratory infections using chest Computed Tomography (CT) scan images has gained much attention after the COVID-19 pandemic. Major tasks while building such models require an understanding of the characteristic features associated with the disease, patient-to-patient variations and changes associated with disease severity. In this work, an attention-based Convolutional Neural Network (CNN) model with customized bottleneck residual module (Attn-CNN) is proposed for classifying CT images into three classes: COVID-19, Normal, and other Pneumonia. The efficacy of the model is evaluated by carrying out various experiments, such as effect of class imbalance, impact of attention module, generalizability of the model and providing visualization of model’s prediction for the interpretability of results. Comparative performance evaluation with five state-of-the-art deep architectures such as MobileNet, EfficientNet-B7, Inceptionv3, ResNet-50 and VGG-16, and with published models such as COVIDNet-CT, COVNet, COVID-Net CT2, etc. is discussed.

Abstract

Breast cancer remains a significant global health concern, and machine learning algorithms and computer-aided detection systems have shown great promise in enhancing the accuracy and efficiency of mammography image analysis. However, there is a critical need for large, benchmark datasets for training deep learning models for breast cancer detection. In this work we developed Mammo-Bench, a large-scale benchmark dataset of mammography images, by collating data from seven well-curated resources, viz., INbreast, Mini-DDSM, KAU-BCMD, CMMD, CDD-CESM, DMID, and RSNA Screening Dataset. To ensure consistency across images from diverse sources while preserving clinically relevant features, all the images underwent a preprocessing pipeline that includes breast segmentation, pectoral muscle removal, and intelligent cropping. The dataset consists of 71,844 high-quality mammographic images from 26,500 patients across 8 countries and is one of the largest open-source mammography databases to the best of our knowledge. To show the utility of Mammo-Bench, ResNet101 architecture was used for classifying the images into Normal, Benign and Malignant classes. Performance of ResNet101 was evaluated on the proposed dataset and the results compared with a few member datasets and an external dataset, VinDr-Mammo. We show that training on the larger, proposed benchmark dataset is more reliable compared to when trained on other smaller datasets. An accuracy of 78.8% (with data augmentation of the minority classes) and 77.8% (without data augmentation) was achieved on the proposed benchmark dataset, compared to the other datasets for which the accuracy varied from 25 – 69%. Most striking was the improved prediction of the minority classes using the Mammo-Bench. These results establish baseline performance and demonstrate Mammo-Bench's utility as a comprehensive resource for developing and evaluating mammography analysis systems.

Abstract

In shotgun sequencing, the input string (typically, a long DNA sequence composed of nucleotide bases) is sequenced as multiple overlapping fragments of much shorter lengths (called textit{reads}). Modelling the shotgun sequencing pipeline as a communication channel for DNA data storage, the capacity of this channel was identified in a recent work, assuming that the reads themselves are noiseless substrings of the original sequence. Modern shotgun sequencers however also output quality scores for each base read, indicating the confidence in its identification. Bases with low quality scores can be considered to be erased. Motivated by this, we consider the textit{shotgun sequencing channel with erasures}, where each symbol in any read can be independently erased with some probability $delta$. We identify achievable rates for this channel, using a random code construction and a decoder that uses typicality-like arguments to merge the reads.

Abstract

DNA methylation and gene expression are two critical aspects of the epigenetic landscape that contribute significantly to cancer pathogenesis. Analysis of aberrant genome-wide methylation patterns can provide insights into how these affect the cancer transcriptome and possible clinical implications for cancer diagnosis and treatment. The role of tumor suppressors and oncogenes is well known in tumorigenesis. Epigenetic alterations can significantly impact the expression and function of these critical genes, contributing to the initiation and progression of cancer. The present protocol chapter presents a unified workflow to explore the role of DNA methylation in gene expression regulation in breast cancer by identifying differentially expressed genes whose promoter or gene body regions are differentially methylated using various Bioconductor packages in R environment. Functional enrichment analysis of these genes can help in understanding the mechanisms leading to tumorigenesis due to epigenetic alterations.

Abstract

Computer tomography (CT) have been routinely used for the diagnosis of lung diseases and recently, during the pandemic, for detecting the infectivity and severity of COVID-19 disease. One of the major concerns in using machine learning (ML) approaches for automatic processing of CT scan images in clinical setting is that these methods are trained on limited and biased subsets of publicly available COVID-19 data. This has raised concerns regarding the generalizability of these models on external datasets, not seen by the model during training. To address some of these issues, in this work CT scan images from confirmed COVID-19 data obtained from one of the largest public repositories, COVIDx CT 2A were used for training and internal validation of machine learning models. For the external validation we generated Indian-COVID-19 CT dataset, an open-source repository containing 3D CT volumes and 12096 chest CT images from 288 COVID-19 patients from India. Comparative performance evaluation of four state-of-the-art machine learning models, viz., a lightweight convolutional neural network (CNN), and three other CNN based deep learning (DL) models such as VGG-16, ResNet-50 and Inception-v3 in classifying CT images into three classes, viz., normal, non-covid pneumonia, and COVID-19 is carried out on these two datasets. Our analysis showed that the performance of all the models is comparable on the hold-out COVIDx CT 2A test set with 90%–99% accuracies (96% for CNN), while on the external Indian-COVID-19 CT dataset a drop in the performance is observed for all the models (8%–19%). The traditional machine learning model, CNN performed the best on the external dataset (accuracy 88%) in comparison to the deep learning models, indicating that a lightweight CNN is better generalizable on unseen data. The data and code are made available at https://github.com/aleesuss/c19.

Abstract

Deep learning assisted disease diagnosis using chest radiology images to assess severity of various respiratory conditions has garnered a lot of attention after the recent COVID-19 pandemic. Understanding characteristic features associated with the disease in radiology images, along with variations observed from patient-to-patient and with the progression of disease, is important while building such models. In this work, we carried out comparative analysis of various deep architectures with the proposed attention-based Convolutional Neural Network (CNN) model with customized bottleneck residual module (Attn-CNN) in classifying chest CT images into three categories, COVID-19, Normal, and Pneumonia. We show that the attention model with fewer parameters achieved better classification performance compared to state-of-the-art deep architectures such as EfficientNet-B7, Inceptionv3, ResNet-50 and VGG-16, and customized models proposed in similar studies such as COVIDNet-CT, CTnet-10, COVID-19Net, etc.

Abstract

Aberrant genome-wide DNA methylation patterns is common in can-cers. Understanding how these affect the transcriptome can provide insights into subtype specific development and progression of tumorigenesis. In this study we carried out genome-wide analysis of DNA methylation and gene expression pro-files in TCGA-BRCA breast cancer samples to propose a novel set of 35 meth-ylation-based prognostic markers that may provide insights to molecular subtype specific disease stratification. Gene-set enrichment and pathway analysis of the predicted markers using MSigDB and DAVID revealed their role in mammary gland development pathway, various signaling pathways (ERBB2, NOTCH, etc.), and other cancer pathways, and show clear association with genes affected by hormone receptor status. We further show the discriminative power of the proposed DNA methylation signature in classifying breast cancer samples into three molecular subtypes, viz., Luminal, HER2-enriched and Triple Negative. An accuracy of 94.12% and MCC of 0.87 is obtained in stratified 5-fold cross-vali-dation for the three-class classification using SVM-RBF.

Abstract

Deep learning assisted disease diagnosis using chest radiol- ogy images to assess severity of various respiratory conditions has gar- nered a lot of attention after the recent COVID-19 pandemic. Under- standing characteristic features associated with the disease in radiology images, along with variations observed from patient-to-patient and with the progression of disease, is important while building such models. In this work, we carried out comparative analysis of various deep architec- tures with the proposed attention-based Convolutional Neural Network (CNN) model with customized bottleneck residual module (Attn-CNN) in classifying chest CT images into three categories, COVID-19, Normal, and Pneumonia. We show that the attention model with fewer parame- ters achieved better classification performance compared to state-of-the- art deep architectures such as EfficientNet-B7, Inceptionv3, ResNet-50 and VGG-16, and customized models proposed in similar studies such as COVIDNet-CT, CTnet-10, COVID-19Net, etc.

Abstract

Diffuse large B-cell lymphoma (DLBCL) is the most common form of non-Hodgkin lymphoma and frequently develops through the accumulation of several genetic variations. With the advancement in high-throughput techniques, in addition to mutations and copy number variations, structural variations have gained importance for their role in genome instability leading to tumorigenesis. In this study, in order to understand the genetics of DLBCL pathogenesis, we carried out a whole-genome mutation profile analysis of eleven human cell lines from germinal-center B-cell-like (GCB-7) and activated B-cell-like (ABC-4) subtypes of DLBCL. Analysis of genetic variations including small sequence variants and large structural variations across the cell lines revealed distinct variation profiles indicating the heterogeneous nature of DLBCL and the need for novel patient stratification methods to design potential intervention strategies. Validation and prognostic significance of the variants was assessed using annotations provided for DLBCL samples in cBioPortal for Cancer Genomics. Combining genetic variations revealed new subgroups between the subtypes and associated enriched pathways, viz., PI3K-AKT signaling, cell cycle, TGF-beta signaling, and WNT signaling. Mutation landscape analysis also revealed drug–variant associations and possible effectiveness of known and novel DLBCL treatments. From the whole-genome-based mutation analysis, our findings suggest putative molecular genetics of DLBCL lymphomagenesis and potential genomics-driven precision treatments. Keywords: diffuse large B-cell lymphoma (DLBCL); copy number variations; sequence variations; structural variations; cell lines; precision medicine

Abstract

Computer tomography (CT) have been routinely used for the diagnosis of lung diseases and recently, during the pandemic, for detecting the infectivity and severity of COVID-19 disease. One of the major concerns in using ma-chine learning (ML) approaches for automatic processing of CT scan images in clinical setting is that these methods are trained on limited and biased sub-sets of publicly available COVID-19 data. This has raised concerns regarding the generalizability of these models on external datasets, not seen by the model during training. To address some of these issues, in this work CT scan images from confirmed COVID-19 data obtained from one of the largest public repositories, COVIDx CT 2A were used for training and internal vali-dation of machine learning models. For the external validation we generated Indian-COVID-19 CT dataset, an open-source repository containing 3D CT volumes and 12096 chest CT images from 288 COVID-19 patients from In-dia. Comparative performance evaluation of four state-of-the-art machine learning models, viz., a lightweight convolutional neural network (CNN), and three other CNN based deep learning (DL) models such as VGG-16, ResNet-50 and Inception-v3 in classifying CT images into three classes, viz., normal, non-covid pneumonia, and COVID-19 is carried out on these two datasets. Our analysis showed that the performance of all the models is comparable on the hold-out COVIDx CT 2A test set with 90% - 99% accuracies (96% for CNN), while on the external Indian-COVID-19 CT dataset a drop in the performance is observed for all the models (8% - 19%). The traditional ma-chine