Endomyocardial biopsy (EMB) screening represents the standard of care for detecting allograft rejections after heart transplant. Manual interpretation of EMBs is affected by substantial interobserver and intraobserver variability, which often leads …
Deep Learning-based computational pathology algorithms have demonstrated profound ability to excel in a wide array of tasks that range from characterization of well known morphological phenotypes to predicting non human-identifiable features from …
Using co-attention mapping between WSIs and genomic to capture multimodal interactions between histology images and genes for predicting patient survival. By adapting Transformer layers as a general encoder backbone in MIL, we consistently outperform current SOTA for survival prediction across 5 different cancer datasets (4,730 WSIs, 67 million patches).
Using weakly-supervised AI to predict the primary origin of metastatic tumors, with application to Cancers of Unknown Primary (CUP).
Data-efficient and interpretable classification of digitized histopathology slides using only slide-level training labels, with application to cancer subtyping, metastasis detection and more.
An integrated framework for fusing histopathology and genomic features for cancer diagnosis and prognosis.