All Competitors

Scalable and transferable U-Net family (14M–1.4B parameters) for 3D medical image segmentation, supervised-pretrained on TotalSegmentator.

Medical image segmentation model that solves unseen segmentation tasks in context from a few labeled examples, with no retraining or fine-tuning.

Biomedical vision-language model trained contrastively on 1.6M figure-caption pairs mined from PubMed Central open-access articles.

Self-supervised pretraining for 3D medical images that learns anatomical correspondences between scans, giving encoders transferable to segmentation.

Biomedical vision-language model trained contrastively on 15M PubMed Central figure-caption pairs for zero-shot classification, retrieval, and VQA.

Self-supervised pretraining framework for medical imaging that unifies pixel restoration with contrastive learning across 2D and 3D image backbones.

Text-conditioned latent diffusion model that generates synthetic chest X-rays from free-form radiology prompts by adapting Stable Diffusion.

3D CT segmentation of abdominal organs and tumors, where a deformable-attention Transformer decodes organ embeddings into the segmentation kernels.

Brain MRI segmentation network with progressive levels of detail, trained across ~160 acquisition sites so one checkpoint handles unseen scanners.

Pathology instance segmentation for glomeruli, nuclei and eosinophils, deforming a bounding circle into a contour rather than a box into an octagon.

Chest CT translation model that synthesizes a virtual expiratory scan from one inspiratory volume, so small airways disease needs no second scan.

Human-in-the-loop cell segmentation framework enabling custom model training from as few as 100-200 corrected annotations.

Medical vision-language pretraining framework that injects structured medical knowledge into radiology image-text learning for VQA and retrieval.

Self-supervised vision-language model for zero-shot detection of chest X-ray pathologies, trained on image-report pairs without explicit labels.

Virtual staining network that turns label-free multiphoton brain-tissue images into H&E and Perls Prussian Blue histology, from unpaired data.

Renal pathology segmentation covering six kidney tissue types across 5x to 40x magnifications from one network, and transferring from human to mouse.

Glomerular detection, segmentation, and glomerulosclerosis subtyping from renal whole-slide images, pretrained on web-mined glomerular figures.

Generalist deep learning algorithm for cell and nucleus instance segmentation using simulated diffusion flows, without per-dataset retraining.

Multi-organ and tumor segmentation in 3D abdominal CT from a single network whose segmentation kernels are generated per task by a controller.

Brain MRI segmentation model that labels an entire 7T T1w volume in a single pass, returning six tissue classes plus background in seconds.

Glomerulus and nuclei detection in whole-slide pathology images, predicting a bounding circle rather than a box for rotation-consistent localization.

Self-supervised 3D pretrained models for CT and MRI that learn anatomical representations from unlabeled volumes and transfer to segmentation tasks.

Pretrained 3D-ResNet backbones for volumetric medical image analysis, co-trained across eight CT and MRI segmentation datasets for transfer learning.