All Competitors

Breast-specialized multimodal pathology foundation model for core needle biopsy diagnosis, with conformal risk control gating report release.

Genomics foundation model that represents individual DNA fragments in a learned semantic space for cell-free DNA cancer detection and cell typing.

Transcriptome foundation model for precision oncology, generalizing zero-shot across tissue, plasma cfRNA, and tumor-educated platelet modalities.

Multimodal slide-level pathology foundation model trained by clinical-dialogue supervision on 2.3M whole-slide images and 14M Q&A pairs.

Agent-based pathology model that navigates whole-slide images by zooming and panning like a pathologist, scoring 88.6% on the PathMMU-HR2 benchmark.

Cell-free RNA language model for liquid biopsy, fusing RNA sequence embeddings with cfRNA abundance across 306 billion pretraining tokens.

Prostate cancer detection model for MRI and transrectal ultrasound, trained with patch-level contrastive learning across 4,401 patients.

Skin cancer subtype classification from H&E whole slide images, with one vision transformer reading patches at 10x, 20x, 40x and 400x.

DNA methylation foundation model that encodes 5mC as a fifth base, pretrained on 568 million BS-seq reads for tissue-of-origin and expression tasks.

Self-supervised histopathology encoder that models tissue entities as a graph and pretrains by latent graph diffusion over masked subgraphs.

Pathology vision-language model for whole-slide diagnosis, adding lesion detection and segmentation to visual question answering on gigapixel images.

Prostate histopathology classifiers that split H&E tissue into benign, Gleason 3, 4 and 5 patches and aggregate those calls into an ISUP grade group.

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

Vision-language foundation model pre-trained on screening mammogram-report pairs to improve data efficiency and robustness in breast cancer detection.

Histopathology foundation model that encodes 224x224 H&E patches into compact 384-dimensional embeddings for tumor and biomarker classifiers.

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

Histopathology tile encoder pairing a CNN stem with a multi-scale Swin Transformer, pretrained on 15.6 million unlabeled H&E patches.