68 citations · 138 across the 14 of their papers we have counts for
6 papers · 1 filter
From Classification to Cross-Modal Understanding: Leveraging Vision-Language Models for Fine-Grained Renal Pathology
Zhenhao Guo, Rachit Saluja, Tianyuan Yao +14
Fine-grained glomerular subtyping is essential for kidney biopsy interpretation, yet it presents a harder setting than the broad organ- or disease-level recognition tasks commonly…
BackSplit: The Importance of Sub-dividing the Background in Biomedical Lesion Segmentation
Rachit Saluja, Asli Cihangir, Ruining Deng +3
Segmenting small lesions in medical images remains notoriously difficult. Most prior work tackles this challenge by either designing better architectures, loss functions, or data a…
Glo-VLMs: Leveraging Vision-Language Models for Fine-Grained Diseased Glomerulus Classification
Zhenhao Guo, Rachit Saluja, Tianyuan Yao +8
Vision-language models (VLMs) have shown considerable potential in digital pathology, yet their effectiveness remains limited for fine-grained, disease-specific classification task…
BPD-Neo: An MRI Dataset for Lung-Trachea Segmentation with Clinical Data for Neonatal Bronchopulmonary Dysplasia
Rachit Saluja, Arzu Kovanlikaya, Candace Chien +5
Bronchopulmonary dysplasia (BPD) is a common complication among preterm neonates, with portable X-ray imaging serving as the standard diagnostic modality in neonatal intensive care…
BraTS orchestrator : Democratizing and Disseminating state-of-the-art brain tumor image analysis
Florian Kofler, Marcel Rosier, Mehdi Astaraki +34
The Brain Tumor Segmentation (BraTS) cluster of challenges has significantly advanced brain tumor image analysis by providing large, curated datasets and addressing clinically rele…
Cancer Type, Stage and Prognosis Assessment from Pathology Reports using LLMs
Rachit Saluja, Jacob Rosenthal, Yoav Artzi +3
Large Language Models (LLMs) have shown significant promise across various natural language processing tasks. However, their application in the field of pathology, particularly for…