Weakly Supervised and Deep Learning Methods for Histopathological Image Classification in Neurological and Renal Disorders

R Anirudh Reddy

Abstract

The analysis of digital histopathology slides or Whole Slide Images (WSIs) is critical for several diagnoses. Recent advancements in computational techniques, particularly in the field of digital pathology, have shown promise in automating the classification process. Whole Slide Imaging (WSI), combined with deep learning and modern computer vision techniques, has emerged as a powerful tool in this domain. This thesis addresses two major medical challenges using deep learning and computer vision techniques: the classification of Lupus Nephritis (LN) and low-grade gliomas into their respective subtypes. Systemic lupus erythematosus (SLE) is an autoimmune disease wherein the patient’s immune system attacks healthy tissues, leading to Lupus Nephritis (LN), a severe condition causing renal failure. Traditional methods for diagnosing LN require meticulous pathological assessment of renal biopsies, which is time-consuming. In the first architecture (chapter 3), We propose a novel pipeline that automates this process by: 1) detecting various glomerular patterns in WSIs using Periodic Acid-Schiff (PAS) stained images, and 2) classifying each image based on these extracted glomerular features. This approach leverages deep learning to improve the accuracy and efficiency of LN classification. Low-grade glioma, a type of brain tumor originating from glial cells, also presents significant diagnostic challenges due to the large size and complexity of WSIs. In the second architecture(chapter 4), our work involves the classification of low-grade gliomas into Astrocytoma and Oligodendroglioma. Given the computational infeasibility of training deep learning models on gigapixel images, we adopt a weakly supervised method to extract discriminative patches from WSIs, which represent the tumor regions. A Convolutional Neural Network (CNN) is then trained on these discriminative patches, and the results are aggregated to determine the WSI label. Evaluated on a dataset of 581,616 patches from 286 WSIs obtained from The Cancer Genome Atlas (TCGA) portal, our method achieved a slide-wise accuracy of 79.31%, which increased to 89.65% when trained only on discriminative patches.The methodologies presented in this thesis not only demonstrate significant improvements in classification accuracy but also offer scalable and efficient solutions for enhancing the diagnostic processes in pathology, ultimately contributing to better patient outcomes and more efficient healthcare deliver.

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