Qilin-Med-VL: Towards Chinese Large Vision-Language Model for General Healthcare

• URL: http://arxiv.org/abs/2310.17956v2
• Date: Wed, 1 Nov 2023 07:10:23 GMT
• Title: Qilin-Med-VL: Towards Chinese Large Vision-Language Model for General Healthcare
• Authors: Junling Liu, Ziming Wang, Qichen Ye, Dading Chong, Peilin Zhou, Yining Hua
• Abstract summary: This study introduces Qilin-Med-VL, the first Chinese large vision-language model designed to integrate the analysis of textual and visual data. We also release ChiMed-VL, a dataset consisting of more than 1M image-text pairs.
• Score: 14.646414629627001
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large Language Models (LLMs) have introduced a new era of proficiency in comprehending complex healthcare and biomedical topics. However, there is a noticeable lack of models in languages other than English and models that can interpret multi-modal input, which is crucial for global healthcare accessibility. In response, this study introduces Qilin-Med-VL, the first Chinese large vision-language model designed to integrate the analysis of textual and visual data. Qilin-Med-VL combines a pre-trained Vision Transformer (ViT) with a foundational LLM. It undergoes a thorough two-stage curriculum training process that includes feature alignment and instruction tuning. This method enhances the model's ability to generate medical captions and answer complex medical queries. We also release ChiMed-VL, a dataset consisting of more than 1M image-text pairs. This dataset has been carefully curated to enable detailed and comprehensive interpretation of medical data using various types of images.

Related papers

• LLaVA-Ultra: Large Chinese Language and Vision Assistant for Ultrasound [7.941670191244354]
  We propose a fine-grained adaptive VLM architecture for Chinese medical visual conversations through parameter-efficient tuning. Specifically, we devise a fusion module with fine-grained vision encoders to achieve enhancement for subtle medical visual semantics. For execution, we leverage a large-scale multimodal Chinese ultrasound dataset obtained from the hospital.
  arXiv  Detail & Related papers  (2024-10-19T11:38:31Z)
• LoGra-Med: Long Context Multi-Graph Alignment for Medical Vision-Language Model [55.80651780294357]
  State-of-the-art medical multi-modal large language models (med-MLLM) leverage instruction-following data in pre-training. LoGra-Med is a new multi-graph alignment algorithm that enforces triplet correlations across image modalities, conversation-based descriptions, and extended captions. Our results show LoGra-Med matches LLAVA-Med performance on 600K image-text pairs for Medical VQA and significantly outperforms it when trained on 10% of the data.
  arXiv  Detail & Related papers  (2024-10-03T15:52:03Z)
• 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)
• 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)
• Vision-Language Models for Medical Report Generation and Visual Question Answering: A Review [0.0]
  Medical vision-language models (VLMs) combine computer vision (CV) and natural language processing (NLP) to analyze medical data. Our paper reviews recent advancements in developing models designed for medical report generation and visual question answering.
  arXiv  Detail & Related papers  (2024-03-04T20:29:51Z)
• MedSumm: A Multimodal Approach to Summarizing Code-Mixed Hindi-English Clinical Queries [16.101969130235055]
  We introduce the Multimodal Medical Codemixed Question Summarization MMCQS dataset. This dataset combines Hindi-English codemixed medical queries with visual aids. Our dataset, code, and pre-trained models will be made publicly available.
  arXiv  Detail & Related papers  (2024-01-03T07:58:25Z)
• CLIPSyntel: CLIP and LLM Synergy for Multimodal Question Summarization in Healthcare [16.033112094191395]
  We introduce the Multimodal Medical Question Summarization (MMQS) dataset. This dataset pairs medical queries with visual aids, facilitating a richer and more nuanced understanding of patient needs. We also propose a framework, consisting of four modules that identify medical disorders, generate relevant context, filter medical concepts, and craft visually aware summaries.
  arXiv  Detail & Related papers  (2023-12-16T03:02:05Z)
• Masked Vision and Language Pre-training with Unimodal and Multimodal Contrastive Losses for Medical Visual Question Answering [7.669872220702526]
  We present a novel self-supervised approach that learns unimodal and multimodal feature representations of input images and text. The proposed approach achieves state-of-the-art (SOTA) performance on three publicly available medical VQA datasets.
  arXiv  Detail & Related papers  (2023-07-11T15:00:11Z)
• Customizing General-Purpose Foundation Models for Medical Report Generation [64.31265734687182]
  The scarcity of labelled medical image-report pairs presents great challenges in the development of deep and large-scale neural networks. We propose customizing off-the-shelf general-purpose large-scale pre-trained models, i.e., foundation models (FMs) in computer vision and natural language processing.
  arXiv  Detail & Related papers  (2023-06-09T03:02:36Z)
• 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)
• UC2: Universal Cross-lingual Cross-modal Vision-and-Language Pre-training [52.852163987208826]
  UC2 is the first machine translation-augmented framework for cross-lingual cross-modal representation learning. We propose two novel pre-training tasks, namely Masked Region-to-Token Modeling (MRTM) and Visual Translation Language Modeling (VTLM) Our proposed framework achieves new state-of-the-art on diverse non-English benchmarks while maintaining comparable performance to monolingual pre-trained models on English tasks.
  arXiv  Detail & Related papers  (2021-04-01T08:30:53Z)

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.