Computational Pathology

Deep learning-enabled assessment of cardiac allograft rejection from endomyocardial biopsies

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 …

Federated learning for computational pathology on gigapixel whole slide images

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 …

Multimodal Co-Attention Transformer for Survival Prediction in Gigapixel Whole Slide Images

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).

Multiplex computational pathology for treatment response prediction

A spotlight article on recent advances in multiplex immunofluorescence (mIF) and the potential of combining machine learning and mIF assays for clinical outcome prediction and biomarker discovery.

AI-based pathology predicts origins for cancers of unknown primary

Using weakly-supervised AI to predict the primary origin of metastatic tumors, with application to Cancers of Unknown Primary (CUP).

Data-efficient and weakly supervised computational pathology on whole-slide images

Data-efficient and interpretable classification of digitized histopathology slides using only slide-level training labels, with application to cancer subtyping, metastasis detection and more.

Pathomic Fusion: an integrated framework for fusing histopathology and genomic features for cancer diagnosis and prognosis

An integrated framework for fusing histopathology and genomic features for cancer diagnosis and prognosis.

Weakly supervised prostate tma classification via graph convolutional networks

Semi-supervised histology classification using deep multiple instance learning and contrastive predictive coding