[month] [year]

Amit Sharma

Amit Sharma supervised by Dr. Vinod Palakkad Krishnanunni  received his Master of Science by Research in Computer Science and Engineering (CSE). Here’s a summary of his research work on Enhancing Diagnostic Accuracy and Prognostic Assessment through Computational Pathology:

Lupus Nephritis (LN), a severe manifestation of systemic lupus erythematosus (SLE), presents significant diagnostic challenges due to the complex nature of renal pathology. Traditional methods for LN classification are labour-intensive, requiring glomerular-level labelling of whole slide images (WSIs), which leads to high inter- and intra-observer variability, lengthy turnaround times, and difficulty in identifying molecular biomarkers and morphological features. These issues highlight the need for automated and efficient methods that can handle the immense size and complexity of WSIs. Similarly, traditional immune scoring methods for assessing immune responses within colon tissues are also labour-intensive and prone to variability. These challenges highlight the need for automated and efficient diagnostic techniques that can handle the immense size and complexity of WSIs and provide accurate diagnosis and prognosis. This thesis addresses these challenges through two main contributions: the development of LupusNet, a novel deep-learning model for LN classification using slide-level labels, and the creation of an open-source web-based tool for the automatic calculation of Immunoscore. LupusNet leverages Multiple Instance Learning (MIL) and advanced attention mechanisms to classify LN subtypes without requiring detailed glomerular annotations. The proposed method demonstrates significant improvements in classification accuracy, achieving an AUC score of 91.0%, an F1 score of 77.3%, and an accuracy of 81.1% on a comprehensive dataset of multi-stained LN digital histopathology WSIs from the Indian population. In parallel, a web-based application for immunoscoring has been developed, allowing users to upload WSIs and annotations for Core of Tumor (CT) and Invasive Margin (IM). The integrated algorithm calculates the immunoscore for individual CT and IM regions, only quantifying the immune response within the annotated tissue region. This pipeline is designed to be user-friendly and accessible, facilitating its use in clinical and research settings. Our pipeline is an end to-end solution for calculating the Immunoscore, overcoming several limitations of already available tools. Extensive experiments and evaluations of both LupusNet and the immunoscoring pipeline highlight their effectiveness and potential for practical integration into clinical workflows. The findings underscore the importance of leveraging digital pathology and machine learning to improve diagnostic accuracy, reduce pathologist workload, and enhance the scalability of LN classification and immune scoring applications. The contributions of this research provide valuable insights and tools for the field of medical image analysis, with implications for advancing the diagnosis and treatment of complex diseases such as LN and enhancing the overall process of immunotherapy treatments.

December 2024