All Competitors

Plant genome foundation model for ab initio gene structure annotation, predicting genes, coding sequences, and exons at single-nucleotide resolution.

Multi-sequence spine MRI foundation model with DINOv3 encoders, supporting condition classification, pathology localization, and report generation.

Cryo-EM ligand modeling pipeline that detects bound ligand densities in a map, then reconstructs their atomic structures with a diffusion model.

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

Self-supervised brain MRI foundation model built on DINOv3, pretrained on roughly 6.6 million unlabeled axial slices for neuroimaging tasks.

Vision transformer trained with DINO self-supervision to segment micronuclei in DNA-stained fluorescence images across cell lines and microscopes.

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

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

Digital hematopathology foundation model unifying blood-cell detection, classification, segmentation, and visual question answering.

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

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

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.

Modality-agnostic foundation model for human brain imaging that runs five core neuroimaging tasks across uncalibrated CT and MRI without retraining.

Family of CNN foundation models pretrained on multimodal radiology images, a domain-specific alternative to ImageNet transfer learning weights.

Multimodal clinical foundation model reasoning jointly over 2D and 3D medical images, ECG time-series, and text reports across nine clinical domains.

Generalist cell segmentation model pairing SAM's ViT-L encoder with Cellpose flow fields, outperforming average human annotators on its benchmark.

Brain MRI segmentation foundation model trained on 66,000+ image-label pairs across 14 MRI sub-modalities, with a hypergraph dynamic adapter.

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.

3D MRI organ segmentation foundation model built on Swin-UNETR and trained on the UKBOB whole-body dataset covering 72 organs and skeletal structures.

Universal ultrasound segmentation foundation model adapting the Segment Anything Model to eight anatomical regions in a single promptable network.

Whole-brain axon and soma segmentation foundation model and registration pipeline for developmental connectomics, with no per-stage retraining.

Vision-language foundation model for fetal ultrasound, pretrained on 210,035 image-text pairs for plane classification, biometry, and segmentation.