All Competitors

Multimodal medical imaging foundation model for zero-shot clinical diagnosis and report generation from chest X-ray and CT in English and Chinese.

Multimodal ECG language model pairing a specialized signal encoder with a biomedical LLM for cardiovascular disease detection and question answering.

Multi-transformer model that predicts tissue-specific alternative splicing outcomes and generalizes zero-shot to unseen cellular conditions.

Multimodal vision-text foundation model for brain CT and MRI, pretrained on roughly 10 million image-report pairs to act as a clinical copilot.

Vision-language foundation model for precision oncology, pretrained on 50M pathology images and 1B text tokens via unified masked modeling.

Cryo-ET segmentation framework adapting SAM2 to vesicles and membrane-bound compartments in tomograms and 2D micrographs, zero-shot or fine-tuned.

Biomedical multimodal LLM that answers questions about medical images and returns pixel-level segmentation masks, using a mixture-of-experts design.

Multimodal foundation model integrating protein sequence, structure, and natural language to model and generate protein phenotypes across scales.

Genomic foundation model with 7B parameters that models prokaryotic DNA, RNA, and protein at single-nucleotide resolution over a 131k-token context.

Conditional autoregressive genomic language model trained on 13.6M mammalian promoters, scoring promoter variants, including indels, zero-shot.

Audio-language foundation model for bioacoustics that answers natural-language questions about animal sounds, with zero-shot species classification.

Cell phenotyping model for spatial proteomics using a language-informed vision transformer to classify cell types zero-shot across marker panels.

Protein language models trained on biophysical dynamics from MD simulations and normal-mode analysis; ESMDance builds on ESM2 for variant effects.

Text-guided MRI synthesis model that generates brain MR sequences and resolutions on demand from routine scans using imaging-metadata prompts.

SAM2-based foundation model that segments 2D and 3D medical images by treating volumes and image sets as video object tracking.

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

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

Histopathology vision-language foundation model pretrained on 1.17 million image-caption pairs with contrastive and captioning objectives.

Foundation model for medical image registration that aligns CT and MRI across anatomies and modalities without per-pair optimization or retraining.

Self-supervised foundation model for functional brain network analysis from resting-state fMRI, pretrained across 30 datasets for disorder diagnosis.

Promptable foundation model for universal medical image segmentation, fine-tuned from SAM on 1.57M image-mask pairs across 10 imaging modalities.

Self-supervised foundation models for wearable PPG and ECG signals, trained with contrastive learning on Apple Heart and Movement Study recordings.

Vision-language pretraining for 3D CT volumes, aligning scans with their radiology reports for zero-shot classification, retrieval, and segmentation.

Vision foundation model for the tree of life, trained on TreeOfLife-10M for zero-shot species classification of plants, animals, and fungi.