All Competitors

Vision-language model for annotation-free pathology localization, marking the finding a text prompt names in X-ray, histology and fundus images.

Pathology foundation model that aligns whole-slide images with genomic, epigenetic, and transcriptomic data for patient-level tumor representations.

Chest X-ray conversational assistant that fine-tunes LLaVA-Med on instruction data enriched with predictions from expert radiograph classifiers.

Explainable autoencoder for transcriptome analysis that uses SHAP attribution on latent variables to identify critical genes driving gene expression.

Gastric pathology model reading whole-slide images through a chain of dependent questions, mirroring a pathologist's stepwise reasoning.

Histopathology foundation model for whole-slide cancer diagnosis, with a ViT-L encoder pretrained by DINOv2 on a million-slide hospital archive.

Histology nuclei segmentation that adapts SAM to train on several datasets at once, aligning auxiliary domains without diluting the primary one.

RNA language model that predicts secondary structure of internal ribosome entry sites from sequence alone, trained on roughly 50,000 IRES sequences.

All-atom structure prediction for arbitrary biomolecular complexes of proteins, nucleic acids, and ligands, with code and weights under a BSD license.

Learned compression autoencoders for histopathology whole-slide images, tuned so reconstructions preserve the features downstream models rely on.

Biomolecular structure prediction foundation model covering proteins, small molecules, DNA, RNA, and glycans in a single diffusion framework.

Multi-task antibody developability model predicting 18 biophysical endpoints from heavy- and light-chain sequence, trained on Lilly assay data.

Multimodal all-atom generative model for zero-shot de novo antibody and protein-binder design, validated by wet-lab hit rates from small batches.

Graph attention foundation model for spatial transcriptomics that assigns spatial domains zero-shot across gene panels, tissues, and technologies.

Multitask peptide model predicting strain-specific minimum inhibitory concentrations against 34 bacteria directly from an amino acid sequence.

Histopathology foundation model for whole-slide cancer diagnosis, covering 19 common cancer types and 205 clinical diagnostic tasks.

Health acoustics foundation model that turns short clips of coughs and breaths into embeddings for building acoustic biomarker models with less data.

Slide-level pathology foundation model turning whole-slide images into reusable embeddings for classification, retrieval, and report generation.

Splice-site and variant-impact prediction from DNA sequence, with pretrained models for human, mouse, zebrafish, honey bee, and Arabidopsis.

Chest X-ray vision-language model that drafts the findings section of a radiology report, at 7B parameters small enough to run on a single GPU.

Attention-based multiple instance learning heads for whole-slide pathology, pretrained on a 108-way pan-cancer slide classification task.

Protein binder design model post-trained from a multimodal protein language model to bind proteins, peptides, small molecules, and nucleic acids.

Pathology vision foundation model adapting DINOv3 self-supervised learning to whole-slide histopathology across many magnifications and scales.