All Competitors

Lightweight mixture-of-experts medical vision-language model routing visual question answering and image classification to domain-specific experts.

Swin transformer foundation model for fluorescence microscopy image restoration, unifying denoising, super-resolution, and volumetric reconstruction.

Multimodal large language model for 3D medical imaging that handles report generation, visual question answering, and segmentation on CT volumes.

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

Self-supervised 3D CT foundation model that extracts general-purpose tumor representations for cancer imaging biomarker discovery and prognosis.

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

Self-supervised pretraining framework for 3D medical image encoders that learns anatomy by predicting where a sub-volume sits within a CT scan.

Instruction-tuned vision-language foundation model for chest X-ray interpretation, with 8 billion parameters spanning eight clinical task types.

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

Google's dermatology image embedding model that produces 6144-dimensional embeddings for data-efficient skin-condition classifiers.

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.

Radiology-specific multimodal LLM that generates the findings section of a chest X-ray report from a frontal image, pairing RAD-DINO with Vicuna-7B.

Promptable 3D foundation model for volumetric CT segmentation, covering over 200 anatomical categories through point, box, and free-text prompts.

Universal cell segmentation model adapting Meta's SAM to segment mammalian cells, yeast, and bacteria across imaging modalities without retraining.

Fully 3D promptable segmentation foundation model for volumetric CT and MR, encoding whole volumes so anatomy can be segmented from one prompt point.

Chest X-ray vision-language model that generates free-text radiology reports, pairing a CXR-specific image encoder with a 7B LLaMA-2 language model.

Large-scale chest X-ray vision-language pretraining model that learns image-report alignment for zero-shot and few-shot radiograph classification.

Multi-modal ophthalmic foundation model for generalist eye AI, spanning fundus imaging and OCT for disease screening, segmentation, and biomarkers.

Abdominal CT segmentation model driven by CLIP text embeddings, covering 25 organs and 6 tumor types with zero-shot extension to new categories.

Self-supervised foundation model for retinal imaging, pretrained on 1.6 million unlabelled fundus and OCT scans to detect ocular and systemic disease.

Vision-language pre-training framework for universal brain MRI diagnosis, learning from imaging-report pairs to cover more than ten brain diseases.

Medical imaging adaptation of the Segment Anything Model, fine-tuned on 4.6M images and 19.7M masks for promptable segmentation across 10 modalities.

Vision-language foundation model for pathology, fine-tuned from CLIP on 208,414 image-text pairs for zero-shot classification and image retrieval.