All Competitors

Cryo-ET particle picking model that averages tiny, medium, and large 3D U-Nets pretrained on simulated tomograms and fine-tuned on experimental data.

Generalist cell segmentation pairing the cyto3 super-generalist model with one-click networks that denoise, deblur, and upsample microscopy images.

Cryo-ET particle picking ensemble of three 3D segmentation models predicting particle-center heatmaps with ResNet50d and EfficientNetV2-M backbones.

Cryo-ET particle picking model, an ensemble of 3D U-Nets with EfficientNet encoders that finds protein complexes in tomograms by heatmap segmentation.

Cryo-ET particle picking model that localizes six protein complexes in tomograms using an ensemble of lightweight 3D U-Nets.

Segment Anything Model finetuned on diverse medical images, giving a reusable promptable checkpoint for interactive and automatic image segmentation.

Multimodal vision-text foundation model for brain CT and MRI, pretrained on roughly 10 million image-report pairs to act as a clinical copilot.

Cryo-ET segmentation framework adapting SAM2 to vesicles and membrane-bound compartments in tomograms and 2D micrographs, zero-shot or fine-tuned.

Cryo-ET particle picking model that localizes and classifies multiple protein complexes in a tomogram with a single 3D U-Net forward pass.

Biomedical multimodal LLM that answers questions about medical images and returns pixel-level segmentation masks, using a mixture-of-experts design.

Promptable ultrasound image segmentation foundation model, a SAM adaptation trained on US-43d, the largest public ultrasound segmentation corpus.

Biomedical imaging foundation model that segments, detects, and recognizes structures across nine modalities from natural language prompts.

Masked-autoencoder foundation model that pre-trains a 3D Residual Encoder U-Net on roughly 39,000 brain MRIs for volumetric image segmentation.

Self-supervised vision transformer pretrained on chest X-rays to produce a domain-specific foundation model for classification and lung segmentation.

CT segmentation foundation model that uses task prompts to segment 83 anatomical structures and lesions across whole-body scans in a single network.

Chest X-ray foundation model that pairs masked image modeling with image-report contrastive alignment for zero-shot diagnosis and phrase grounding.

SAM2-based foundation model that segments 2D and 3D medical images by treating volumes and image sets as video object tracking.

Ultrasound foundation model pretrained on over two million multi-organ images, transferring to segmentation, classification, and image enhancement.

Interactive foundation model for biomedical image segmentation, prompted with scribbles, clicks, and bounding boxes to segment unseen structures.

Semi-supervised cryo-ET segmentation framework that adapts DINOv2 vision transformers for 3D organelle annotation using sparse 2D slice labels.

DeepLabV3 segmentation model that separates tissue from glass background in H&E and IHC whole-slide images, as used by the HEST-Library.

3D vision-transformer foundation model for multimodal neuroimage segmentation, pretrained self-supervised on brain MRI from 41,400 participants.

Medical image segmentation foundation model for 3D CT and MRI, covering 127 anatomical classes automatically plus interactive point-prompt refinement.

Virtual staining models that translate label-free light microscopy into fluorescent-equivalent predictions of nuclei and plasma membranes.