All Competitors

Medical multimodal LLM (2B and 8B) trained for generalizable, step-by-step clinical reasoning via Mentor-Intern Collaborative Search.

Contrastive language-image model for fMRI functional decoding, predicting cognitive tasks, concepts, and domains from brain activation maps.

Vision foundation model for MRI, pretrained on 6.9 million slices across 18 body locations for label-efficient segmentation and classification.

Spatiotemporal foundation model that learns representations directly from 4D functional MRI volumes for disease diagnosis and phenotype prediction.

Generalist medical multimodal LLM for image understanding, visual question answering, and report generation across twelve-plus imaging modalities.

Generative histopathology foundation model: a diffusion transformer trained on 30M H&E tiles, conditioned on self-supervised slide embeddings.

Multimodal clinical foundation model reasoning jointly over 2D and 3D medical images, ECG time-series, and text reports across nine clinical domains.

Vision foundation model for the tree of life, trained on 214 million organism images across 952,000 taxa for zero-shot species classification.

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

Distribution-level representation learning that embeds whole cell populations, perturbation responses, and sequence sets, not individual data points.

Cytogenetics foundation model detecting numerical and structural chromosome abnormalities from metaphase images, pretrained on 84,000 specimens.

Raman spectroscopy foundation model that denoises biological spectra and learns embeddings transferable across cell, tissue, and tumor studies.

Retinal fundus foundation model conditioned on patient age and sex, pretrained on 1.0 million colour photographs from 292,000 patients.

Surgical video foundation model pretrained by entropy-maximizing compression on 0.78M unlabeled frames from 2,122 minimally invasive procedures.

Predicts 16-channel multiplex immunofluorescence from H&E histology using a ViT foundation-model encoder, validated on five external datasets.

Latent diffusion model for H&E-to-IHC stain transfer, dual-conditioned on pathology foundation-model embeddings, covering HER2, Ki67, ER, and PR.

Sparse-view CBCT reconstruction foundation model pretrained on 8,407 CT volumes, recovering full 3D anatomy from as few as six X-ray projections.

Zero-shot tumor segmentation on CT and MRI that reads text-prompted anomaly attention maps out of a frozen medical foundation diffusion model.

Brain MRI segmentation foundation model trained on 66,000+ image-label pairs across 14 MRI sub-modalities, with a hypergraph dynamic adapter.

Generalist cell segmentation model pairing SAM's ViT-L encoder with Cellpose flow fields, outperforming average human annotators on its benchmark.

Universal foundation model that jointly generates diagnostic text and segments the corresponding targets across ten biomedical imaging modalities.

Multi-resolution vision-language foundation model for histopathology, pretrained on 34M TCGA image-text pairs across four magnifications.

Functional ultrasound reconstruction with neural operators, rebuilding power Doppler brain images from 10-15% of the usual compounded frames.