All Competitors

Uncertainty-aware diffusion model that enhances cryo-EM density maps while estimating voxel-wise confidence via Monte Carlo sampling.

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

Protein pretraining framework that learns representations directly from cryo-EM density maps, transferring to flexibility, active-site, binding-affinity, and stability tasks.

Channel-adaptive autoregressive generative model that synthesizes in-silico fluorescence microscopy of protein subcellular localization from amino-acid sequence and cellular landmark stains.

A physics-informed generative foundation model for quantitative diffusion MRI that maps brain microstructure (tensor, kurtosis, NODDI) and adapts zero-shot to each participant's data.

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 Transformer-based streamline propagation model for mouse-brain diffusion-MRI tractography, trained on Allen Mouse Brain Connectivity Atlas streamlines guided by viral tracer data.

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

Deep generative model simulating fluorescence microscopy images for all 12,800 human proteins across three landmark stains, providing proteome-wide virtual cell imaging at single-cell resolution.

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

A self-supervised, cell-centric pretraining strategy that distills morphology and microenvironment views of each cell into a unified embedding for virtual spatial omics from microscopy.

A Joint-Embedding Predictive foundation model for echocardiography, pretrained on 18M cardiac ultrasound videos to learn artifact-robust anatomical representations.

Bidirectional generative framework linking single-cell nuclear morphology and gene expression, built on a morphology foundation model trained on 21M+ segmented nuclei.

A NAFNet backbone trained with a perceptual GAN objective for high-fidelity bioimage restoration, achieving best LPIPS on 7 of 8 AI4Life benchmarks.

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.

An all-in-one foundation model for microscopic image restoration, unifying 8 tasks across 5 microscopy modalities and 2D/3D data, with zero-shot inference on unseen imaging systems.

Flow-matching generative model that simulates high-resolution fluorescence images of human fibroblasts conditioned on surface micro-topographies, a digital twin of cell-material interactions.

A generalist neuroimaging vision foundation model pretrained on 5.24M clinical MRI and CT volumes for radiologic diagnosis and report generation.

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

A self-supervised foundation model for structural brain MRI combining DenseNet and Vision Transformer with Mixture of Experts for multi-task brain disease diagnosis and brain age prediction.

A neuro-oncology foundation model for brain tumor MRI that uses distributionally robust self-supervised pretraining to predict molecular markers and survival across institutions.

A SimCLR self-supervised foundation model for 3D brain MRI, pretrained on 18,759 patients across 11 neurological-disease datasets for diverse diagnostic tasks.

A Mamba-based dual-branch UNet that enhances cryo-EM and cryo-ET density maps using local resolution-guided learning to improve interpretability.