26 citations · 70 across the 6 of their papers we have counts for
5 papers · 1 filter
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…
Towards Spatial Transcriptomics-driven Pathology Foundation Models
Konstantin Hemker, Andrew H. Song, Cristina Almagro-Pérez +6
Spatial transcriptomics (ST) provides spatially resolved measurements of gene expression, enabling characterization of the molecular landscape of human tissue beyond histological a…
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…
Molecular-driven Foundation Model for Oncologic Pathology
Anurag Vaidya, Andrew Zhang, Guillaume Jaume +15
Foundation models are reshaping computational pathology by enabling transfer learning, where models pre-trained on vast datasets can be adapted for downstream diagnostic, prognosti…
HEST-1k: A Dataset for Spatial Transcriptomics and Histology Image Analysis
Guillaume Jaume, Paul Doucet, Andrew H. Song +8
Spatial transcriptomics enables interrogating the molecular composition of tissue with ever-increasing resolution and sensitivity. However, costs, rapidly evolving technology, and…