15 citations · 15 across the 4 of their papers we have counts for
4 papers
Dino-NestedUNet: Unlocking Foundation Vision Encoders for Pathology Tumor Bulk Segmentation via Dense Decoding
Tianyang Wang, Ziyu Su, Abdul Rehman Akbar +7
Vision foundation models (VFMs), such as DINOv3, provide rich semantic representations that are promising for computational pathology. However, many current adaptations pair frozen…
Unified Multi-Foundation-Model Slide Representation for Pan-Cancer Recognition and Text-Guided Tumor Localization
Tianyang Wang, Ziyu Su, Abdul Rehman Akbar +6
The expanding ecosystem of pathology foundation models has produced powerful but fragmented tile-level representations, limiting their use in clinical tasks that require unified sl…
MorphDistill: Distilling Unified Morphological Knowledge from Pathology Foundation Models for Colorectal Cancer Survival Prediction
Hikmat Khan, Usama Sajjad, Metin N. Gurcan +4
Background: Colorectal cancer (CRC) remains a leading cause of cancer-related mortality worldwide. Accurate survival prediction is essential for treatment stratification, yet exist…
A Foundational Multimodal Vision Language AI Assistant for Human Pathology
Ming Y. Lu, Bowen Chen, Drew F. K. Williamson +8
The field of computational pathology has witnessed remarkable progress in the development of both task-specific predictive models and task-agnostic self-supervised vision encoders.…