All Competitors

Text-to-image diffusion model that generates synthetic medical images across imaging modalities and organs to augment scarce clinical training data.

Grounded multimodal language model for endoscopic surgery, supporting visual dialogue, region-based question answering, and bounding-box grounding.

Segment Anything Model finetuned on diverse medical images, giving a reusable promptable checkpoint for interactive and automatic image segmentation.

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

Variational autoencoder that learns interpretable representations of protein subtomograms from cryo-ET, trained on 5.8 million synthetic particles.

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

Cryo-ET particle picking model that localizes and classifies multiple protein complexes in a tomogram with a single 3D U-Net forward pass.

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

Bilingual Arabic-English medical multimodal model built on Llama 3.1 for radiology, CT, and histology image understanding and question answering.

Vision transformers trained on Human Protein Atlas fluorescence microscopy for subcellular protein localization and cell morphology representation.

Tissue-aware foundation model that restores brain MRI quality across motion correction, super-resolution, denoising, and harmonization.

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

Promptable ultrasound image segmentation foundation model, a SAM adaptation trained on US-43d, the largest public ultrasound segmentation corpus.

Biomedical imaging foundation model that segments, detects, and recognizes structures across nine modalities from natural language prompts.

Cell Painting microscopy foundation model, a channel-agnostic masked autoencoder producing morphological embeddings for zero-shot phenotypic analysis.

Cryo-EM foundation model pre-trained on 65 million particle images, enabling zero-shot classification, pose clustering, and quality assessment.

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

Masked-autoencoder foundation model that pre-trains a 3D Residual Encoder U-Net on roughly 39,000 brain MRIs for volumetric image segmentation.

Self-supervised vision transformer pretrained on chest X-rays to produce a domain-specific foundation model for classification and lung segmentation.

Multimodal large language model that interprets 12-lead electrocardiogram images, answering open-ended clinical questions and generating ECG reports.

Diffusion model translating in both directions between protein sequences and fluorescence microscopy images to predict subcellular localization.

Self-supervised contrastive model embedding cell and organelle dynamics from time-lapse microscopy for cell-state analysis without manual labels.

CT segmentation foundation model that uses task prompts to segment 83 anatomical structures and lesions across whole-body scans in a single network.

Generative foundation model for cryo-EM density maps using flow matching, enabling zero-shot denoising, map sharpening, and missing wedge restoration.