8 citations · 23 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…
VIPER: An Expert-Curated Benchmark for Vision-Language Models in Veterinary Pathology
Luca L. Weishaupt, Simone de Brot, Javier Asin +9
Pathology vision-language models are advancing rapidly, yet existing benchmarks remain focused on human tissue, particularly oncology, leaving non-human pathology largely unaddress…
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…
Evidence-based diagnostic reasoning with multi-agent copilot for human pathology
Luca L. Weishaupt, Chengkuan Chen, Drew F. K. Williamson +8
Pathology is experiencing rapid digital transformation driven by whole-slide imaging and artificial intelligence (AI). While deep learning-based computational pathology has achieve…
AI-driven 3D Spatial Transcriptomics
Cristina Almagro-Pérez, Andrew H. Song, Luca Weishaupt +13
A comprehensive three-dimensional (3D) map of tissue architecture and gene expression is crucial for illuminating the complexity and heterogeneity of tissues across diverse biomedi…
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…