8 citations · 16 across the 5 of their papers we have counts for
6 papers
Beyond Sparse Captions: Aligning Slide-Level Text and Patch-Level Vision in Pathology
Daniel Shao, Dongmin Bang, Luca L. Weishaupt +6
Contrastive vision-language (VL) pretraining in computational pathology has generally relied on curated image-caption datasets sourced from social media, educational videos, and re…
Sparse concept attribution for histomorphological hypothesis generation from whole-slide classifiers
Tristan Lazard, Kenza Bouzid, Julius Hense +6
Histology images contain rich morphological information and can provide insights into pathological processes. However, deriving hypotheses relating morphological phenotypes to clin…
Mixture of Mini Experts: Overcoming the Linear Layer Bottleneck in Multiple Instance Learning
Daniel Shao, Joel Runevic, Richard J. Chen +4
Multiple Instance Learning (MIL) is the predominant framework for classifying gigapixel whole-slide images in computational pathology. MIL follows a sequence of 1) extracting patch…
Do Multiple Instance Learning Models Transfer?
Daniel Shao, Richard J. Chen, Andrew H. Song +4
Multiple Instance Learning (MIL) is a cornerstone approach in computational pathology (CPath) for generating clinically meaningful slide-level embeddings from gigapixel tissue imag…
A Foundation Model for Spatial Proteomics
Muhammad Shaban, Yuzhou Chang, Huaying Qiu +57
Foundation models have begun to transform image analysis by acting as pretrained generalist backbones that can be adapted to many tasks even when post-training data are limited, ye…
A General-Purpose Self-Supervised Model for Computational Pathology
Richard J. Chen, Tong Ding, Ming Y. Lu +17
Tissue phenotyping is a fundamental computational pathology (CPath) task in learning objective characterizations of histopathologic biomarkers in anatomic pathology. However, whole…