Our latest science, research and ideas

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

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

PyDESeq2: a python package for bulk RNA-seq differential expression analysis

The use of deep learning models to predict progression-free survival in patients with neuroendocrine tumors

Response heterogeneity as a new biomarker of treatment response in patients with neuroendocrine tumors

Scaling Self-Supervised Learning for Histopathology with Masked Image Modeling

Deep learning predicts patients outcome and mutations from digitized histology slides in gastrointestinal stromal tumor

Adjuvant endocrine therapy uptake, toxicity, quality of life, and prediction of early discontinuation