RESEARCH, PUBLICATIONS & NEWS

SAINT has been studied across multiple cancer types, with results published in peer-reviewed journals and collaborations at Northeastern University and the Roux Institute. These studies explore how explainable AI supports exploratory analysis in digital pathology research.

Peer-reviewed publications in digital pathology research

Colorectal cancer

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

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)

Gastric cancer

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

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)

Media & news coverage

Vincent Harris, Saeed Amal honored by National Academy of Inventors

Northeastern Global News
March 25, 2025

Can AI help with breast cancer diagnoses? Northeastern researchers develop new system that is nearly 100% accurate

Northeastern Global News
June 24, 2024

New AI tool can help health care providers detect prostate cancer faster, Northeastern researcher says

Northeastern Global News
January 9, 2024