MedTrinity-25M: A Large-scale Multimodal Dataset with Multigranular Annotations for Medicine

• URL: http://arxiv.org/abs/2408.02900v1
• Date: Tue, 6 Aug 2024 02:09:35 GMT
• Title: MedTrinity-25M: A Large-scale Multimodal Dataset with Multigranular Annotations for Medicine
• Authors: Yunfei Xie, Ce Zhou, Lang Gao, Juncheng Wu, Xianhang Li, Hong-Yu Zhou, Sheng Liu, Lei Xing, James Zou, Cihang Xie, Yuyin Zhou,
• Abstract summary: This paper introduces MedTrinity-25M, a comprehensive, large-scale multimodal dataset for medicine. It covers over 25 million images across 10 modalities, with multigranular annotations for more than 65 diseases. Unlike existing approach which is limited by the availability of image-text pairs, we have developed the first automated pipeline.
• Score: 53.01393667775077
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper introduces MedTrinity-25M, a comprehensive, large-scale multimodal dataset for medicine, covering over 25 million images across 10 modalities, with multigranular annotations for more than 65 diseases. These enriched annotations encompass both global textual information, such as disease/lesion type, modality, region-specific descriptions, and inter-regional relationships, as well as detailed local annotations for regions of interest (ROIs), including bounding boxes, segmentation masks. Unlike existing approach which is limited by the availability of image-text pairs, we have developed the first automated pipeline that scales up multimodal data by generating multigranular visual and texual annotations (in the form of image-ROI-description triplets) without the need for any paired text descriptions. Specifically, data from over 90 different sources have been collected, preprocessed, and grounded using domain-specific expert models to identify ROIs related to abnormal regions. We then build a comprehensive knowledge base and prompt multimodal large language models to perform retrieval-augmented generation with the identified ROIs as guidance, resulting in multigranular texual descriptions. Compared to existing datasets, MedTrinity-25M provides the most enriched annotations, supporting a comprehensive range of multimodal tasks such as captioning and report generation, as well as vision-centric tasks like classification and segmentation. Pretraining on MedTrinity-25M, our model achieves state-of-the-art performance on VQA-RAD and PathVQA, surpassing both multimodal large language models and other representative SoTA approaches. This dataset can also be utilized to support large-scale pre-training of multimodal medical AI models, contributing to the development of future foundation models in the medical domain.

Related papers

• UniMed-CLIP: Towards a Unified Image-Text Pretraining Paradigm for Diverse Medical Imaging Modalities [68.12889379702824]
  Vision-Language Models (VLMs) trained via contrastive learning have achieved notable success in natural image tasks. UniMed is a large-scale, open-source multi-modal medical dataset comprising over 5.3 million image-text pairs. We trained UniMed-CLIP, a unified VLM for six modalities, achieving notable gains in zero-shot evaluations.
  arXiv  Detail & Related papers  (2024-12-13T18:59:40Z)
• MRGen: Diffusion-based Controllable Data Engine for MRI Segmentation towards Unannotated Modalities [59.61465292965639]
  This paper investigates a new paradigm for leveraging generative models in medical applications. We propose a diffusion-based data engine, termed MRGen, which enables generation conditioned on text prompts and masks.
  arXiv  Detail & Related papers  (2024-12-04T16:34:22Z)
• MedViLaM: A multimodal large language model with advanced generalizability and explainability for medical data understanding and generation [40.9095393430871]
  We introduce MedViLaM, a unified vision-language model towards a generalist model for medical data. MedViLaM can flexibly encode and interpret various forms of medical data, including clinical language and imaging. We present instances of zero-shot generalization to new medical concepts and tasks, effective transfer learning across different tasks, and the emergence of zero-shot medical reasoning.
  arXiv  Detail & Related papers  (2024-09-29T12:23:10Z)
• A Refer-and-Ground Multimodal Large Language Model for Biomedicine [10.519866875035003]
  The Med-GRIT-270k dataset is the first dedicated to the biomedical domain and integrates refer and ground conversations. We introduce a Refer-and-Ground Multimodal Large Language Model for Biomedicine (BiRD) by using this dataset and multi-task instruction learning.
  arXiv  Detail & Related papers  (2024-06-26T07:56:17Z)
• Medical Vision-Language Pre-Training for Brain Abnormalities [96.1408455065347]
  We show how to automatically collect medical image-text aligned data for pretraining from public resources such as PubMed. In particular, we present a pipeline that streamlines the pre-training process by initially collecting a large brain image-text dataset. We also investigate the unique challenge of mapping subfigures to subcaptions in the medical domain.
  arXiv  Detail & Related papers  (2024-04-27T05:03:42Z)
• RadGenome-Chest CT: A Grounded Vision-Language Dataset for Chest CT Analysis [56.57177181778517]
  RadGenome-Chest CT is a large-scale, region-guided 3D chest CT interpretation dataset based on CT-RATE. We leverage the latest powerful universal segmentation and large language models to extend the original datasets.
  arXiv  Detail & Related papers  (2024-04-25T17:11:37Z)
• Med-MoE: Mixture of Domain-Specific Experts for Lightweight Medical Vision-Language Models [17.643421997037514]
  We propose a novel framework that tackles both discriminative and generative multimodal medical tasks. The learning of Med-MoE consists of three steps: multimodal medical alignment, instruction tuning and routing, and domain-specific MoE tuning. Our model can achieve performance superior to or on par with state-of-the-art baselines.
  arXiv  Detail & Related papers  (2024-04-16T02:35:17Z)
• C^2M-DoT: Cross-modal consistent multi-view medical report generation with domain transfer network [67.97926983664676]
  We propose a cross-modal consistent multi-view medical report generation with a domain transfer network (C2M-DoT) C2M-DoT substantially outperforms state-of-the-art baselines in all metrics.
  arXiv  Detail & Related papers  (2023-10-09T02:31:36Z)
• Large Scale Multi-Lingual Multi-Modal Summarization Dataset [26.92121230628835]
  We present the current largest multi-lingual multi-modal summarization dataset (M3LS) It consists of over a million instances of document-image pairs along with a professionally annotated multi-modal summary for each pair. It is also the largest summarization dataset for 13 languages and consists of cross-lingual summarization data for 2 languages.
  arXiv  Detail & Related papers  (2023-02-13T18:00:23Z)

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.