4 papers
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…
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…
Multimodal Whole Slide Foundation Model for Pathology
Tong Ding, Sophia J. Wagner, Andrew H. Song +20
The field of computational pathology has been transformed with recent advances in foundation models that encode histopathology region-of-interests (ROIs) into versatile and transfe…
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…