All Competitors

Medical vision-language model that unifies image comprehension and generation in one autoregressive transformer via heterogeneous LoRA adapters.

Self-supervised CT foundation model that segments pathology as visual anomalies, pretrained on 30,000 unlabeled 3D volumes with no annotation.

Cervical cytology screening system pretrained on 127,471 whole-slide images from 48 centers, with test-time adaptation for new clinical sites.

865M-parameter multimodal foundation model that fuses 3D low-dose chest CT with clinical data to answer 17 lung cancer screening questions.

Nuclei detection and classification in histopathology whole slide images, replacing segmentation masks with direct transformer-based set prediction.

Pan-tumour CT foundation model pretrained on 30,000 synthetic 3D scans carrying lesion masks and structured reports across ten organ systems.

End-to-end transformer reading fluorescence microscopy video to return single-molecule trajectories with Hurst exponents and diffusion coefficients.

Multimodal medical imaging foundation model for zero-shot clinical diagnosis and report generation from chest X-ray and CT in English and Chinese.

Medical image segmentation model that replaces MedSAM's manual box prompts with a diffusion prompt encoder and labels each mask by class.

Phenotypic screening model for neuronal activity, encoding network-level calcium dynamics of iPSC-derived neurons into single-cell embeddings.

Self-supervised 3D vision foundation model for non-contrast head CT, pretrained on 361,663 scans to detect a broad range of intracranial disease.

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

Segment Anything fine-tuned for nucleus segmentation in histopathology, supporting automatic and interactive annotation on unseen tissue images.

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

Prostate cancer detection model for MRI and transrectal ultrasound, trained with patch-level contrastive learning across 4,401 patients.

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

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

Translates whole-brain imaging phenotypes between humans and mice through a shared latent space built from transcriptomics and connectivity.

Histopathology feature extractor distilled from a 1.1B-parameter teacher into a ViT-Base, with leading robustness to staining and scanner variation.

Biomedical vision-language model aligning image regions to UMLS clinical concepts, for zero-shot diagnosis across 10 imaging modalities.

Histopathology image translation model that standardizes H&E staining style, then generates virtual collagen, reticulin, and trichrome fiber images.

Grounded multimodal language model for endoscopic surgery, supporting visual dialogue, region-based question answering, and bounding-box grounding.