26 citations · 82 across the 11 of their papers we have counts for
4 papers · 2 filters
Multimodal Prototyping for cancer survival prediction
Andrew H. Song, Richard J. Chen, Guillaume Jaume +3
Multimodal survival methods combining gigapixel histology whole-slide images (WSIs) and transcriptomic profiles are particularly promising for patient prognostication and stratific…
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…
Morphological Prototyping for Unsupervised Slide Representation Learning in Computational Pathology
Andrew H. Song, Richard J. Chen, Tong Ding +3
Representation learning of pathology whole-slide images (WSIs) has been has primarily relied on weak supervision with Multiple Instance Learning (MIL). However, the slide represent…
Transcriptomics-guided Slide Representation Learning in Computational Pathology
Guillaume Jaume, Lukas Oldenburg, Anurag Vaidya +5
Self-supervised learning (SSL) has been successful in building patch embeddings of small histology images (e.g., 224x224 pixels), but scaling these models to learn slide embeddings…