Our latest science, research and ideas

Distilling foundation models for robust and efficient models in digital pathology

AI allows pre-screening of FGFR3 mutational status using routine histology slides of muscle-invasive bladder cancer

Deep learning uncovers histological patterns of YAP1/TEAD activity related to disease aggressiveness in cancer patients

FedPyDESeq2: a federated framework for bulk RNA-seq differential expression analysis

Transcriptome Analysis of Archived Tumor Tissues by Visium, GeoMx DSP, and Chromium Methods Reveals Inter- and Intra-Patient Heterogeneity

Legitimate ground-truth-free metrics for deep uncertainty classification scoring

Phikon-v2, A large and public feature extractor for biomarker prediction

MYC Rearrangement Prediction from LYSA Whole Slide Images in Large B-cell Lymphoma: A Multi-centric Validation of Self-supervised Deep Learning Models

CARMIL: Context-Aware Regularization on Multiple Instance Learning models for Whole Slide Images

A deep learning-based multiscale integration of spatial omics with tumor morphology.

Development of a Deep Learning model Tailored for HER2 Detection in Breast Cancer to aid pathologists in interpreting HER2-Low cases

Robust Evaluation of Deep Learning-based Representation Methods for Survival and Gene Essentiality Prediction on Bulk RNA-seq Data