6 citations · 11 across the 10 of their papers we have counts for
11 papers
Gated Spatial Redundancy Projection for Pathology Transformer Attentions
Zhiyuan Yang, Jiahao Cheng, Vincent Quoc-Huy Trinh +1
Transformer models are increasingly used for whole-slide image analysis in computational pathology. Yet, WSIs differ fundamentally from natural images: neighbouring patches often c…
Simple Token-Efficient Vision-Language Model for Case-level Pathology Synoptic Report Generation
Zhiyuan Yang, Jiahao Cheng, Vincent Quoc-Huy Trinh +1
Generating clinically useful pathology reports for pathology cases from whole-slide images (WSIs) is challenging due to gigapixel resolution, long visual-token sequences, and the c…
MOOZY: A Patient-First Foundation Model for Computational Pathology
Yousef Kotp, Vincent Quoc-Huy Trinh, Christopher Pal +1
Computational pathology needs whole-slide image (WSI) foundation models that transfer across diverse clinical tasks, yet current approaches remain largely slide-centric, often depe…
AtlasPatch: Scalable Foundation Model-based Tissue Detection and Patch Extraction for Computational Pathology
Ahmed Alagha, Christopher Leclerc, Yousef Kotp +12
Whole-slide image (WSI) preprocessing, including tissue detection and patch extraction, is critical computational pathology, yet remains a major bottleneck for large-scale workflow…
ADPv2: A Hierarchical Histological Tissue Type-Annotated Dataset for Potential Biomarker Discovery of Colorectal Disease
Zhiyuan Yang, Kai Li, Sophia Ghamoshi Ramandi +9
Computational pathology (CoPath) leverages histopathology images to enhance diagnostic precision and reproducibility in clinical pathology. However, publicly available datasets for…
Investigating Zero-Shot Diagnostic Pathology in Vision-Language Models with Efficient Prompt Design
Vasudev Sharma, Ahmed Alagha, Abdelhakim Khellaf +2
Vision-language models (VLMs) have gained significant attention in computational pathology due to their multimodal learning capabilities that enhance big-data analytics of giga-pix…