Bhole Gaurav Hitesh supervised by Prof. Nita Parekh received his Master of Science – Dual Degree in Computational Natural Sciences (CND). Here’s a summary of his research work on From Pixels to Bases: Benchmarking Infrastructure and Deep Learning Frameworks for Multi-Scale Breast Cancer Characterization

Breast cancer is among the most prevalent and clinically heterogeneous malignancies worldwide, and its characterization at multiple biological scales such as radiological phenotype and genomic architecture represents one of the defining computational challenges at the interface of information science and biomedicine. Despite significant algorithmic progress in both medical image analysis and genomic data processing, reliable computational characterization of breast cancer is constrained by a common and underappreciated bottleneck, which is the absence of rigorous, standardized benchmarking infrastructure that enables reproducible evaluation and fair comparison of methods. This thesis addresses two distinct but complementary computational problems in breast cancer research: (i) the development and evaluation of benchmark datasets and deep learning frameworks for mammographic image analysis, and (ii) the systematic benchmarking of long-read sequencing tools for structural variant detection in germline and somatic cancer contexts. Both problems are unified by the same underlying bottleneck, which is the absence of rigorous evaluation infrastructure. The contributions presented here address this bottleneck at both scales. The first contribution is Mammo-Bench, a large-scale unified mammography benchmark dataset comprising 19,731 images from 6,500 patients across six geographically diverse publicly available resources, standardized through a comprehensive preprocessing pipeline and annotated for five clinical tasks including cancer status, breast density, BI-RADS score, abnormality type, and molecular subtype. Mammo-Bench is, to the best of our knowledge, the largest open-access multi-task mammography benchmark currently available and establishes a reproducible evaluation standard for mammographic computer-aided detection and diagnosis systems. The second contribution is MammoInsight, a unified multi-task deep learning framework for mammographic analysis, trained on an extended benchmark of 65,602 images. Built on the MedImageInsight foundation model backbone, MammoInsight simultaneously performs five clinical classification tasks and lesion segmentation in a single forward pass, incorporating SAM-guided region-of-interest pooling, cross-view attention fusion, and ordinal regression losses for clinically ordered tasks. Evaluated against four large foundation model baselines, MammoInsight achieves state-of-the-art performance on cancer status prediction, breast density classification, and BI-RADS scoring, and attains a segmentation Dice score of 0.804, substantially outperforming a fine-tuned SAM-Med2D baseline (0.734) despite using only 367 million parameters compared to baselines up to 74 times larger. The third contribution is a systematic multi-context evaluation of long-read structural variant (SV) callers across three experimental paradigms, which include simulated chromosome 17 data at four coverage levels, germline benchmarking against the GIAB HG002 Tier 1 truth set, and paired tumor-normal somatic analysis of HER2+ (HCC1954) and triple-negative breast cancer (HCC1937) cell lines on both ONT PromethION R10 and PacBio Revio platforms. Six germline callers and four dedicated somatic callers are evaluated using a novel three-tier benchmarking framework that accounts for systematic differences in VCF output representation across tools. Key findings include caller-specific coverage thresholds for reliable SV detection (F1 > 0.85 achievable at 5x for Sniffles2, Kled, and SVDF), near-ceiling germline performance at high coverage, and a consistent, substantial performance gap between dedicated somatic callers and germline callers adapted through normal subtraction in the cancer context. The thesis establishes benchmarking infrastructure and validates computational frameworks across the imaging and genomic modalities of breast cancer characterization and provides guidance for tool selection and evaluation design in both research and clinical genomics applications. 

June 2026