Our latest science, research and ideas

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

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

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

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

Subgroup analysis methods for time-to-event outcomes in heterogeneous randomized controlled trials

Combining a deep learning model with clinical data better predicts hepatocellular carcinoma behavior following surgery

FedECA: A Federated External Control Arm Method for Causal Inference with Time-To-Event Data in Distributed Settings

Validation of MSIntuit as an AI-based pre-screening tool for MSI detection from colorectal cancer histology slides