UniBiomed: A Universal Foundation Model for Grounded Biomedical Image Interpretation

• URL: http://arxiv.org/abs/2504.21336v2
• Date: Thu, 29 May 2025 05:14:48 GMT
• Title: UniBiomed: A Universal Foundation Model for Grounded Biomedical Image Interpretation
• Authors: Linshan Wu, Yuxiang Nie, Sunan He, Jiaxin Zhuang, Luyang Luo, Neeraj Mahboobani, Varut Vardhanabhuti, Ronald Cheong Kin Chan, Yifan Peng, Pranav Rajpurkar, Hao Chen,
• Abstract summary: We introduce UniBiomed, the first universal foundation model for grounded biomedical image interpretation.<n>UniBiomed is capable of generating accurate diagnostic findings and simultaneously segmenting the corresponding biomedical targets.<n>To develop UniBiomed, we curate a large-scale dataset comprising over 27 million triplets of images, region annotations, and text descriptions.
• Score: 18.550642453062228
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The integration of AI-assisted biomedical image analysis into clinical practice demands AI-generated findings that are not only accurate but also interpretable to clinicians. However, existing biomedical AI models generally lack the ability to simultaneously generate diagnostic findings and localize corresponding biomedical objects. This limitation makes it challenging for clinicians to correlate AI-generated findings with visual evidence (e.g., tiny lesions) in images and interpret the results of AI models. To address this challenge, we introduce UniBiomed, the first universal foundation model for grounded biomedical image interpretation, which is capable of generating accurate diagnostic findings and simultaneously segmenting the corresponding biomedical targets. UniBiomed is based on a novel integration of Multi-modal Large Language Model and Segment Anything Model, which can effectively unify diverse biomedical tasks in universal training for advancing grounded interpretation. To develop UniBiomed, we curate a large-scale dataset comprising over 27 million triplets of images, region annotations, and text descriptions across ten biomedical imaging modalities. Extensive validation on 70 internal and 14 external datasets demonstrated the state-of-the-art performance of UniBiomed in diverse biomedical tasks, including image segmentation, disease recognition, region-aware diagnosis, vision question answering, and report generation. In summary, UniBiomed is a powerful and versatile biomedical foundation model, unlocking the untapped grounded interpretation capability for optimizing AI-assisted biomedical image analysis.

Related papers

• iMedImage Technical Report [5.0953390013898705]
  Chromosome karyotype analysis is crucial for diagnosing hereditary diseases, yet detecting structural abnormalities remains challenging.<n>We developed iMedImage, an end-to-end model for general medical image recognition, demonstrating strong performance across multiple imaging tasks.
  arXiv  Detail & Related papers  (2025-03-27T03:25:28Z)
• Biomedical Foundation Model: A Survey [84.26268124754792]
  Foundation models are large-scale pre-trained models that learn from extensive unlabeled datasets.<n>These models can be adapted to various applications such as question answering and visual understanding.<n>This survey explores the potential of foundation models across diverse domains within biomedical fields.
  arXiv  Detail & Related papers  (2025-03-03T22:42:00Z)
• An Explainable Biomedical Foundation Model via Large-Scale Concept-Enhanced Vision-Language Pre-training [40.16314726875265]
  ConceptCLIP is the first explainable biomedical foundation model that achieves state-of-the-art diagnostic accuracy. We develop ConceptCLIP through a novel dual-alignment approach that simultaneously learns global image-text representations and fine-grained region-concept associations.
  arXiv  Detail & Related papers  (2025-01-26T16:07:11Z)
• BIOMEDICA: An Open Biomedical Image-Caption Archive, Dataset, and Vision-Language Models Derived from Scientific Literature [73.39593644054865]
  BIOMEDICA is a scalable, open-source framework to extract, annotate, and serialize the entirety of the PubMed Central Open Access subset into an easy-to-use, publicly accessible dataset. Our framework produces a comprehensive archive with over 24 million unique image-text pairs from over 6 million articles. BMCA-CLIP is a suite of CLIP-style models continuously pretrained on the BIOMEDICA dataset via streaming, eliminating the need to download 27 TB of data locally.
  arXiv  Detail & Related papers  (2025-01-13T09:58:03Z)
• BiomedCoOp: Learning to Prompt for Biomedical Vision-Language Models [2.2585213273821716]
  We propose BiomedCoOp, a novel prompt learning framework for biomedical image analysis.<n>Our approach achieves effective prompt context learning by leveraging semantic consistency with average prompt ensembles from Large Language Models (LLMs) and knowledge distillation with a statistics-based prompt selection strategy.<n>We conducted comprehensive validation of our proposed framework on 11 medical datasets across 9 modalities and 10 organs against existing state-of-the-art methods, demonstrating significant improvements in both accuracy and generalizability.
  arXiv  Detail & Related papers  (2024-11-21T19:13:04Z)
• ViKL: A Mammography Interpretation Framework via Multimodal Aggregation of Visual-knowledge-linguistic Features [54.37042005469384]
  We announce MVKL, the first multimodal mammography dataset encompassing multi-view images, detailed manifestations and reports. Based on this dataset, we focus on the challanging task of unsupervised pretraining. We propose ViKL, a framework that synergizes Visual, Knowledge, and Linguistic features.
  arXiv  Detail & Related papers  (2024-09-24T05:01:23Z)
• μ-Bench: A Vision-Language Benchmark for Microscopy Understanding [43.27182445778988]
  Vision-language models (VLMs) offer a promising solution for large-scale biological image analysis. There is a lack of standardized, diverse, and large-scale vision-language benchmarks to evaluate VLMs. mu-Bench is an expert-curated benchmark encompassing 22 biomedical tasks.
  arXiv  Detail & Related papers  (2024-07-01T20:30:26Z)
• BiomedParse: a biomedical foundation model for image parsing of everything everywhere all at once [58.41069132627823]
  holistic image analysis comprises subtasks such as segmentation, detection, and recognition of relevant objects. Here, we propose BiomedParse, a biomedical foundation model for imaging parsing that can jointly conduct segmentation, detection, and recognition for 82 object types across 9 imaging modalities. Through joint learning, we can improve accuracy for individual tasks and enable novel applications such as segmenting all relevant objects in a noisy image through a text prompt.
  arXiv  Detail & Related papers  (2024-05-21T17:54:06Z)
• BioLORD-2023: Semantic Textual Representations Fusing LLM and Clinical Knowledge Graph Insights [15.952942443163474]
  We propose a new state-of-the-art approach for obtaining high-fidelity representations of biomedical concepts and sentences. We demonstrate consistent and substantial performance improvements over the previous state of the art. Besides our new state-of-the-art biomedical model for English, we also distill and release a multilingual model compatible with 50+ languages.
  arXiv  Detail & Related papers  (2023-11-27T18:46:17Z)
• Towards Generalist Biomedical AI [28.68106423175678]
  We introduce Med-PaLM Multimodal (Med-PaLM M), our proof of concept for a generalist biomedical AI system. Med-PaLM M is a large multimodal generative model that flexibly encodes and interprets biomedical data. We conduct a radiologist evaluation of model-generated (and human) chest X-ray reports and observe encouraging performance across model scales.
  arXiv  Detail & Related papers  (2023-07-26T17:52:22Z)
• LLaVA-Med: Training a Large Language-and-Vision Assistant for Biomedicine in One Day [85.19963303642427]
  We propose a cost-efficient approach for training a vision-language conversational assistant that can answer open-ended research questions of biomedical images. The model first learns to align biomedical vocabulary using the figure-caption pairs as is, then learns to master open-ended conversational semantics. This enables us to train a Large Language and Vision Assistant for BioMedicine in less than 15 hours (with eight A100s)
  arXiv  Detail & Related papers  (2023-06-01T16:50:07Z)
• BiomedGPT: A Generalist Vision-Language Foundation Model for Diverse Biomedical Tasks [68.39821375903591]
  Generalist AI holds the potential to address limitations due to its versatility in interpreting different data types. Here, we propose BiomedGPT, the first open-source and lightweight vision-language foundation model.
  arXiv  Detail & Related papers  (2023-05-26T17:14:43Z)
• BiomedCLIP: a multimodal biomedical foundation model pretrained from fifteen million scientific image-text pairs [46.87322157229728]
  We present PMC-15M, a novel dataset that is two orders of magnitude larger than existing biomedical multimodal datasets.<n> PMC-15M contains 15 million biomedical image-text pairs collected from 4.4 million scientific articles.<n>Based on PMC-15M, we have pretrained BiomedCLIP, a multimodal foundation model, with domain-specific adaptations tailored to biomedical vision-language processing.
  arXiv  Detail & Related papers  (2023-03-02T02:20:04Z)
• CBLUE: A Chinese Biomedical Language Understanding Evaluation Benchmark [51.38557174322772]
  We present the first Chinese Biomedical Language Understanding Evaluation benchmark. It is a collection of natural language understanding tasks including named entity recognition, information extraction, clinical diagnosis normalization, single-sentence/sentence-pair classification. We report empirical results with the current 11 pre-trained Chinese models, and experimental results show that state-of-the-art neural models perform by far worse than the human ceiling.
  arXiv  Detail & Related papers  (2021-06-15T12:25:30Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.