All Competitors

Framework turning single-cell expression profiles into ranked gene-name sequences, letting off-the-shelf language models generate and annotate cells.

Interactive foundation model for biomedical image segmentation, prompted with scribbles, clicks, and bounding boxes to segment unseen structures.

Histopathology vision transformer with 1.1B parameters, pretrained on patches from 500,000 H&E whole-slide images across 4,000 clinical practices.

Multimodal vision-language copilot for pathology that answers open-ended questions about histology images and reasons about differential diagnoses.

Training-free cryo-ET segmentation framework that adapts SAM and DINOv2 to 3D volumes, segmenting an entire tomogram from a single point prompt.

Lightweight AlphaFlow variant that fine-tunes only AlphaFold's structure module, keeping the Evoformer frozen to cut conformational sampling cost.

Variational autoencoder pretrained on 74 million human single-cell transcriptomes from the CELLxGENE Census for batch correction and cell typing.

Semi-supervised cryo-ET segmentation framework that adapts DINOv2 vision transformers for 3D organelle annotation using sparse 2D slice labels.

Open medical multimodal LLMs (7B and 34B) for visual question answering over radiology, pathology, and endoscopy images, trained on PubMedVision.

Self-supervised foundation model for 12-lead ECG, pairing masked autoencoder pretraining with contrastive regularization for robust diagnostics.

Multimodal generative protein language model reasoning jointly over protein sequence, structure, and function, trained at 98B parameters.

Self-supervised masked autoencoder for functional MRI that learns representations from BOLD time-series with per-ROI embeddings and graph attention.

DeepLabV3 segmentation model that separates tissue from glass background in H&E and IHC whole-slide images, as used by the HEST-Library.

Respiratory acoustic foundation models pretrained on roughly 136K cough and breathing recordings for disease detection and lung function estimation.

Multimodal model fusing pre-trained DNA, RNA, and protein encoders through cross-attention to predict tissue-specific transcript isoform expression.

3D vision-transformer foundation model for multimodal neuroimage segmentation, pretrained self-supervised on brain MRI from 41,400 participants.

Scaling-law study of protein language models identifying compute-optimal training for causal and masked objectives on 939 million protein sequences.

Histopathology foundation models pretrained with DINOv2 on over 1 million whole-slide images, released as Hibou-B and Hibou-L under Apache 2.0.

Medical image segmentation foundation model for 3D CT and MRI, covering 127 anatomical classes automatically plus interactive point-prompt refinement.

Single-cell foundation model with 800M parameters trained on ~100 million human cells, for annotation, perturbation prediction, and gene analysis.

Single-cell transcriptomics foundation model with 100 million parameters, pretrained on over 50 million human scRNA-seq profiles for cell embeddings.

Microsoft Research multimodal LLM for grounded chest X-ray report generation, localizing each described finding with bounding boxes on the image.

Virtual staining models that translate label-free light microscopy into fluorescent-equivalent predictions of nuclei and plasma membranes.

Multi-task protein framework recasting function, binding site, and structure prediction as autoregressive next-token prediction over ESM2 embeddings.