All Competitors

Histopathology tissue detection model that segments whole-slide thumbnails in a single SAM2 forward pass, replacing patch-wise slide preprocessing.

Joint-embedding predictive foundation model for echocardiography, pretrained on 18M cardiac ultrasound videos for artifact-robust representations.

Bidirectional generative framework linking nuclear morphology and gene expression, built on a morphology model trained on 21 million segmented nuclei.

Bioimage restoration model pairing a NAFNet backbone with a perceptual GAN loss, best on LPIPS in 7 of 8 AI4Life microscopy benchmarks.

Nuclei instance segmentation on H&E whole-slide images, predicting each nucleus as a star-convex polygon with no watershed or NMS separation step.

3D vision-language foundation model for abdominal CT, pretrained on scans, radiology reports, and EHR codes for zero-shot interpretation.

Ab initio heterogeneous cryo-EM reconstruction seeds its encoder with foundation-model priors, sorting 100 structures from one simulated mixture.

Native 3D vision transformer self-supervised on unlabeled fluorescence microscopy volumes, segmenting subcellular structures without voxel labels.

Microscopy image restoration foundation model unifying 8 tasks across 5 modalities and 2D/3D data, with zero-shot inference on unseen systems.

Flow-matching generative model that synthesizes fluorescence images of human fibroblasts conditioned on surface micro-topographies.

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

Self-supervised Siamese network for cryo-electron tomography, enabling zero-shot denoising, segmentation, and macromolecule detection in tomograms.

Synthetic 3D MRI generation spanning brain, prostate, breast and abdomen, with the T1, T2 or FLAIR contrast selected at sampling time.

Vision Transformer foundation model for spatial metabolomics, pretrained on ~4,000 curated METASPACE mass spectrometry imaging datasets.

Clinical laboratory morphology vision-language model that identifies cells, describes them and localizes them in blood, urine and stool microscopy.

Self-supervised foundation model for human cortical cytoarchitecture, encoding histological brain sections into anatomically meaningful features.

Vision-language encoders for chest CT that align 3D volumes with radiology reports using contrastive, report-generation, and masked-image objectives.

Medical vision-language model that takes visual prompts on an image and returns answers grounded in pixel-level segmentation masks.

Single-molecule localisation microscopy encoder that embeds nanoscale point clouds into a 128-dimension latent space to compare protein architecture.

Brain MRI foundation model pairing DenseNet and Vision Transformer backbones with mixture of experts for disease diagnosis and brain age prediction.

Diffusion model for multichannel fluorescent cell microscopy, generating morphologically plausible images aligned to OpenPhenom phenotypic embeddings.

Chest radiograph and report encoder pretrained contrastively on a million pairs, whose frozen embeddings train classifiers from a few hundred cases.

Reference-guided anatomical segmentation for medical images, pairing vision-language spatial reasoning with a co-trained SAM 2 mask decoder.