ArticleNature methods2025
A foundation model for joint segmentation, detection and recognition of biomedical objects across nine modalities.
Article in Nature methods, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers.
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Who cites it
44 citing papers in PubMed.
- AI-based multimodal integration of genomics and electronic health records.Nature reviews. Genetics · 2026Review
- Automated workflow for target volume propagation in online adaptive magnetic resonance guided radiotherapy for lung lesions using foundation models.Physics and imaging in radiation oncology · 2026Article
- PatchChestCT: A patch-level spatial annotation dataset for nine abnormalities in chest CT.Scientific data · 2026Article
- Mitigating hallucinations in synthesized clinical texts to improve multimodal deep learning for dermatology.Journal of biomedical informatics · 2026Article
- Rethinking pathology image analysis through shuffling.npj biomedical innovations · 2026Article
- CLEAR: an auditable foundation model for radiology grounded in clinical concepts.Nature biomedical engineering · 2026Article
- Prototype-based AI triage for 3D pathology.bioRxiv : the preprint server for biology · 2026Article
- Language-Guided Segmentation of Medical Images: A Review of Foundation Models.Bioengineering (Basel, Switzerland) · 2026Review
- Looked but didn't see: inattentional blindness and yes-bias confabulation in vision-language models.medRxiv : the preprint server for health sciences · 2026Article
- Segment Anything for Video: A Comprehensive Review of Video Object Segmentation and Tracking from Past to Future.Neurocomputing · 2026Article
- A universal foundation model for grounded biomedical image interpretation.Nature communications · 2026Article
- Comprehensive evaluation of cross cancer generalization in histopathology segmentation models across 21 tumor types.Communications medicine · 2026Article
- Unsupervised transfer learning enables multi-animal tracking without training annotation.Nature methods · 2026Article
- Vision foundation model for 3D magnetic resonance imaging segmentation, classification, and registration.Medical image analysis · 2026Article
- Decipher-MR: a vision-language foundation model for 3D MRI representations.NPJ digital medicine · 2026Article
- Clinical utility of foundation models in musculoskeletal MRI for biomarker fidelity and predictive outcomes.NPJ digital medicine · 2026Article
- A Self-Supervised Foundation Model Based on Three-Dimensional Chest CT Scans for Lung Cancer Diagnosis and Prognosis Prediction.Radiology. Imaging cancer · 2026Article
- Anticipating Moral and Economic Considerations, Opportunities, and Potential Frictions for AI in Medical Imaging: Multistakeholder Cocreation Study.Journal of medical Internet research · 2026Article
- Bridging modalities with AI: a review of AI advances in multimodal biomedical imaging.Communications engineering · 2026Review
- A multimodal sleep foundation model for disease prediction.Nature medicine · 2026Article
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Authors and funding
16 authors.
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Abstract
Biomedical image analysis is fundamental for biomedical discovery. Holistic image analysis comprises interdependent subtasks such as segmentation, detection and recognition, which are tackled separately by traditional approaches. Here, we propose BiomedParse, a biomedical foundation model that can jointly conduct segmentation, detection and recognition across nine imaging modalities. This joint learning improves the accuracy for individual tasks and enables new applications such as segmenting all relevant objects in an image through a textual description. To train BiomedParse, we created a large dataset comprising over 6 million triples of image, segmentation mask and textual description by leveraging natural language labels or descriptions accompanying existing datasets. We showed that BiomedParse outperformed existing methods on image segmentation across nine imaging modalities, with larger improvement on objects with irregular shapes. We further showed that BiomedParse can simultaneously segment and label all objects in an image. In summary, BiomedParse is an all-in-one tool for biomedical image analysis on all major image modalities, paving the path for efficient and accurate image-based biomedical discovery.
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