All Competitors

Crossmodal diffusion model synthesizing bulk tumor gene expression from H&E whole-slide images, so grading and survival prediction need no RNA assay.

Slide-level pathology foundation model that encodes a whole-slide image of any size into one embedding, supervised by paired sequencing data.

Pretrained variational autoencoder for untargeted blood metabolomics, learning metabolite representations that transfer to clinical prediction tasks.

Self-supervised vision foundation model for structural brain MRI, providing a reusable encoder for brain age, survival, and image classification.

Breast cancer recurrence risk read from an H&E slide and six routine clinical variables, using a frozen pan-cancer pathology encoder.

Weakly supervised histopathology foundation model pretrained on 60,530 whole-slide images for cancer detection, prognosis, and molecular prediction.

Bulk tumor transcriptome model ensembling hundreds of variational autoencoders into interpretable cancer-specific latent spaces for 18 cancers.

Fine-grained cell-type abundance prediction from H&E histology, transferring to unseen cohorts and large slide archives without any retraining.

Self-supervised XCiT encoders for prostate histopathology, pretrained on 48 million tissue tiles so that features cluster by histological pattern.