RESEARCH & PUBLICATIONS

Foundation models are emerging as the standard in self-supervised learning for healthcare applications.  Learning histology patterns within large data sets, using massive feature sets, is a very powerful tool.  See how our innovations have taken shape and led to the groundbreaking support we lend to pathologists.

Peer-reviewed publications in digital pathology:

Colorectal cancer

Vision Transformers Based AI Models For Predicting Colorectal Cancer from Digital Pathology WSI: Use Case Of MHIST dataset (2026)

Gastric cancer

Gastric Cancer Detection with Ensemble Learning on Digital Pathology: Use Case of Gastric Cancer on GasHisSDB Dataset (2024)

 

Prostate cancer

Enhancing Prostate Cancer Diagnosis with a Novel Artificial Intelligence-Based Web Application: Synergizing Deep Learning Models, Multimodal Data, and Insights from Usability Study with Pathologists (2023)

Kidney cancer

Digital Pathology and Ensemble Deep Learning for Kidney Cancer Diagnosis: Dartmouth Kidney Cancer Histology Dataset (2025)

 

Breast cancer

Ensemble Deep Learning-Based Image Classification for Breast Cancer Subtype and Invasiveness Diagnosis from Whole Slide Image Histopathology (2024)

Case studies

Northeastern & PrimaCare research partnership

Northeastern AI researchers partner with Santovia Path AI and Prima CARE to develop cancer detection tools (2025)