All Competitors

Pathology vision-language model that adds lightweight adaptors and multi-granular prompt learning for few-shot whole-slide image classification.

Protein question-answering model that fuses sequence and structure into an LLM prompt as virtual tokens, answering free-form questions about function.

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

Multimodal ECG language model pairing a specialized signal encoder with a biomedical LLM for cardiovascular disease detection and question answering.

DNA language model pretrained jointly on English, protein, and genomic text under one BPE vocabulary, transferring text segmentation skills to DNA.

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

Single-cell analysis model driven by plain-language instructions, covering cell type annotation, pseudo-cell generation, and drug response prediction.

Generative transformer that writes candidate cognate epitope sequences from a TCR CDR3-beta input, annotating repertoires without functional assays.

Genetic language model predicting disease risk and cell-type-specific expression changes from up to 88 megabases of an individual's genome sequence.

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

Vision-language foundation model for precision oncology, pretrained on 50M pathology images and 1B text tokens via unified masked modeling.

Protein-text foundation model aligning sequences with function descriptions through segment-wise objectives for static and dynamic functional sites.

Multi-omics instruction-tuned LLM that reads DNA, RNA, protein, and multi-molecule sequences and answers natural-language questions about them.

Histopathology vision-language foundation model that folds a disease knowledge graph into pretraining for zero-shot cancer detection and subtyping.

Mixed-modal biomedical assistant that generates medical images as well as text, covering radiology and histopathology visual chat and captioning.

Histopathology vision-language model handling image patches and gigapixel slides in one 15B checkpoint, across classification, VQA, and captioning.

Pathology vision-language model for whole-slide diagnosis, adding lesion detection and segmentation to visual question answering on gigapixel images.

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

Multimodal foundation model integrating protein sequence, structure, and natural language to model and generate protein phenotypes across scales.

Bilingual Arabic-English medical multimodal model built on Llama 3.1 for radiology, CT, and histology image understanding and question answering.

Generates free-text descriptions of protein function, catalytic activity, subcellular localization, and domains from sequence alone.

Whole-slide pathology assistant that states the morphological findings behind each diagnosis, trained on 180k VQA pairs from 9,850 gigapixel slides.

Instruction-tuned gene language model extending LLaMA-7B with merged DNA and protein BPE vocabularies to answer sequence tasks as chat prompts.