All Competitors

A two-stage deep learning framework that detects ligand densities in cryo-EM maps and reconstructs their atomic structures with a diffusion generative model.

A domain-specific foundation model for zero-shot plant root image segmentation, built on a MobileSAM backbone and trained across nine diverse root datasets.

A self-supervised DINOv3-based foundation model for brain MRI, pretrained on ~6.6M unlabeled axial slices and transferable to diverse neuroimaging tasks.

A DINO-pretrained vision transformer for accurate, robust segmentation of micronuclei in DNA-stained fluorescence microscopy across cell lines and instruments.

A 3D vision-language foundation model for abdominal CT that pretrains on paired scans, radiology reports, and structured EHR codes for zero-shot interpretation.

A native 3D vision transformer self-supervised on unlabeled fluorescence microscopy volumes that generalizes to unseen object classes without retraining or voxel annotations.

Frontier-scale digital pathology foundation models from PathAI, spanning a compact 22M-parameter variant and a 1.1B-parameter flagship trained on 551K whole-slide images.

A medical vision-language model that accepts visual-referring multimodal input and produces pixel-grounded multimodal output, jointly answering and segmenting medical images.

A modality-agnostic, multi-task foundation model for human brain imaging that runs five core tasks across uncalibrated CT and MRI without retraining.

A family of CNN foundation models pretrained on ~1.9M multimodal radiology images for transfer learning across medical imaging tasks.

Open multimodal clinical foundation model that jointly reasons over medical images, ECG time-series, and text reports, trained with domain-aware reinforcement learning.

Generalist cell segmentation model combining SAM's ViT-L backbone with Cellpose flow fields. First model to surpass average human annotators on the Cellpose benchmark.

A 3D brain MRI segmentation foundation model trained on 66,000+ brain image-label pairs across 14 MRI sub-modalities, paired with a hypergraph dynamic adapter for brain disease analysis.

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

Unified electron microscopy image analysis toolkit built on EM-DINO, a vision foundation model pretrained on 5 million diverse EM images.

A 3D MRI organ segmentation foundation model based on Swin-UNETR, trained on the UKBOB dataset of 1.37 billion labeled masks across 72 anatomical structures.

A foundational model for universal ultrasound image segmentation that adapts the Segment Anything Model to handle eight anatomical regions in a single network.

A CLIP-based vision-language foundation model for fetal ultrasound, pretrained on 210,035 image-caption pairs for plane classification, biometry, anomaly detection, and segmentation.

Fifth-place solution from the CZII CryoET Kaggle competition; an ensemble of four lightweight 3D U-Nets for protein particle localization in cryo-ET tomograms.

Generalist cell segmentation framework with a super-generalist cyto3 model and one-click image restoration networks optimized for downstream segmentation quality.

First-place solution from the CZII CryoET Object Identification Kaggle competition; an ensemble of 3D EfficientNet-encoder U-Nets for multi-class protein particle picking.

Seventh-place CZII CryoET Kaggle solution; an ensemble of three heatmap-predicting 3D segmentation models using ResNet50d and EfficientNetV2-M backbones for particle picking.

Eighth-place CZII CryoET Kaggle solution; a weighted model soup of tiny, medium, and large 3D U-Nets pretrained on simulated data and fine-tuned on experimental cryo-ET tomograms.

A Segment Anything Model (SAM) variant finetuned on diverse medical images, delivering a reusable promptable checkpoint for interactive and automatic medical image segmentation.