activity
20242026
collaborators

8 papers

cs.CV2026

A Multi Center Breast FNAC Whole-Slide Cytology Dataset for AI-Assisted Patch-Wise Classification Using C1 to C5 Reporting Categories

Garima Jain, Abhijeet Patil, Surabhi Jain +36

We present a multi center breast fine needle aspiration cytology (FNAC) dataset designed for patch wise classification using C1 to C5 reporting labels. The prospective dataset incl…

eess.IV2025

Predicting Genetic Mutations from Single-Cell Bone Marrow Images in Acute Myeloid Leukemia Using Noise-Robust Deep Learning Models

Garima Jain, Ravi Kant Gupta, Priyansh Jain +5

In this study, we propose a robust methodology for identification of myeloid blasts followed by prediction of genetic mutation in single-cell images of blasts, tackling challenges…

eess.IV2025

Semantic Segmentation Based Quality Control of Histopathology Whole Slide Images

Abhijeet Patil, Garima Jain, Harsh Diwakar +4

We developed a software pipeline for quality control (QC) of histopathology whole slide images (WSIs) that segments various regions, such as blurs of different levels, tissue regio…

eess.IV2025

A Cytology Dataset for Early Detection of Oral Squamous Cell Carcinoma

Garima Jain, Sanghamitra Pati, Mona Duggal +34

Oral squamous cell carcinoma OSCC is a major global health burden, particularly in several regions across Asia, Africa, and South America, where it accounts for a significant propo…

eess.IV2025

Evaluation Metric for Quality Control and Generative Models in Histopathology Images

Pranav Jeevan, Neeraj Nixon, Abhijeet Patil +1

Our study introduces ResNet-L2 (RL2), a novel metric for evaluating generative models and image quality in histopathology, addressing limitations of traditional metrics, such as Fr…

eess.IV2024

PathoGen-X: A Cross-Modal Genomic Feature Trans-Align Network for Enhanced Survival Prediction from Histopathology Images

Akhila Krishna, Nikhil Cherian Kurian, Abhijeet Patil +2

Accurate survival prediction is essential for personalized cancer treatment. However, genomic data - often a more powerful predictor than pathology data - is costly and inaccessibl…