17 citations · 33 across the 18 of their papers we have counts for
5 papers · 1 filter
WILSON - Multi-magnification composites as a foundation for patient-level pathology analysis and diagnostic text generation
Saghir Alfasly, Wataru Uegami, Sobhan Hemati +17
Pathologists examine tissue sections across multiple magnifications and slides, integrating morphological information to establish a diagnosis. In contrast, most pathology foundati…
Reducing Redundancy in Whole-Slide Image Patching for Scalable Indexing and Retrieval
Jialiang Geng, Ghazal Alabtah, Saghir Alfasly +2
The rapid growth of digital pathology has created an urgent need for efficient indexing and retrieval of whole slide images (WSIs). This need is intensified by emerging generative…
CRISP -- Clustering-Based Redundancy-Reduced Instance Sampling for Pathology Case Representation and Retrieval
Zahra Rahimi Afzal, Wataru Uegami, Saghir Alfasly +6
Digital pathology archives increasingly contain multiple whole-slide images (WSIs) per case, capturing spatially distinct tumor regions and reflecting intrinsic morphological heter…
Validation of Whole-Slide Foundation Models for Image Retrieval in TCGA Data
Tianhao Lei, Parsa Esmaeilkhani, Saghir Alfasly +5
Foundation models are reshaping computational histopathology, yet their value for whole-slide image retrieval relative to strong patch-based and supervised aggregation baselines re…
Retrieval-Guided Generation for Safer Histopathology Image Captioning
Md. Enamul Hoq, Wataru Uegami, Saghir Alfasly +6
Generative vision-language models can produce fluent medical image captions but remain prone to hallucination, over-specific diagnostic claims, and factual inconsistency-serious is…