OphGLM: Training an Ophthalmology Large Language-and-Vision Assistant based on Instructions and Dialogue

• URL: http://arxiv.org/abs/2306.12174v2
• Date: Thu, 22 Jun 2023 01:31:10 GMT
• Title: OphGLM: Training an Ophthalmology Large Language-and-Vision Assistant based on Instructions and Dialogue
• Authors: Weihao Gao, Zhuo Deng, Zhiyuan Niu, Fuju Rong, Chucheng Chen, Zheng Gong, Wenze Zhang, Daimin Xiao, Fang Li, Zhenjie Cao, Zhaoyi Ma, Wenbin Wei, Lan Ma
• Abstract summary: We introduce visual ability into the large language model to complete the ophthalmic large language and vision assistant (OphGLM) Our experimental results demonstrate that the OphGLM model performs exceptionally well, and it has the potential to revolutionize clinical applications in ophthalmology.
• Score: 7.140551103766788
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large multimodal language models (LMMs) have achieved significant success in general domains. However, due to the significant differences between medical images and text and general web content, the performance of LMMs in medical scenarios is limited. In ophthalmology, clinical diagnosis relies on multiple modalities of medical images, but unfortunately, multimodal ophthalmic large language models have not been explored to date. In this paper, we study and construct an ophthalmic large multimodal model. Firstly, we use fundus images as an entry point to build a disease assessment and diagnosis pipeline to achieve common ophthalmic disease diagnosis and lesion segmentation. Then, we establish a new ophthalmic multimodal instruction-following and dialogue fine-tuning dataset based on disease-related knowledge data and publicly available real-world medical dialogue. We introduce visual ability into the large language model to complete the ophthalmic large language and vision assistant (OphGLM). Our experimental results demonstrate that the OphGLM model performs exceptionally well, and it has the potential to revolutionize clinical applications in ophthalmology. The dataset, code, and models will be made publicly available at https://github.com/ML-AILab/OphGLM.

Related papers

• A Survey of Medical Vision-and-Language Applications and Their Techniques [48.268198631277315]
  Medical vision-and-language models (MVLMs) have attracted substantial interest due to their capability to offer a natural language interface for interpreting complex medical data. Here, we provide a comprehensive overview of MVLMs and the various medical tasks to which they have been applied. We also examine the datasets used for these tasks and compare the performance of different models based on standardized evaluation metrics.
  arXiv  Detail & Related papers  (2024-11-19T03:27:05Z)
• Parameter-Efficient Fine-Tuning Medical Multimodal Large Language Models for Medical Visual Grounding [9.144030136201476]
  Multimodal large language models (MLLMs) inherit the superior text understanding capabilities of LLMs and extend these capabilities to multimodal scenarios. These models achieve excellent results in the general domain of multimodal tasks. However, in the medical domain, the substantial training costs and the requirement for extensive medical data pose challenges to the development of medical MLLMs.
  arXiv  Detail & Related papers  (2024-10-31T11:07:26Z)
• LMOD: A Large Multimodal Ophthalmology Dataset and Benchmark for Large Vision-Language Models [38.78576472811659]
  Large vision-language models (LVLMs) have the potential to assist in understanding anatomical information, diagnosing eye diseases, and drafting interpretations and follow-up plans. We benchmarked 13 state-of-the-art LVLM representatives from closed-source, open-source, and medical domains. The results demonstrate a significant performance drop for LVLMs in ophthalmology compared to other domains.
  arXiv  Detail & Related papers  (2024-10-02T14:57:58Z)
• 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)
• EyeCLIP: A visual-language foundation model for multi-modal ophthalmic image analysis [20.318178211934985]
  We propose EyeCLIP, a visual-language foundation model developed using over 2.77 million ophthalmology images with partial text data. EyeCLIP can be transferred to a wide range of downstream tasks involving ocular and systemic diseases.
  arXiv  Detail & Related papers  (2024-09-10T17:00:19Z)
• 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)
• Eye-gaze Guided Multi-modal Alignment for Medical Representation Learning [65.54680361074882]
  Eye-gaze Guided Multi-modal Alignment (EGMA) framework harnesses eye-gaze data for better alignment of medical visual and textual features. We conduct downstream tasks of image classification and image-text retrieval on four medical datasets.
  arXiv  Detail & Related papers  (2024-03-19T03:59:14Z)
• On Large Visual Language Models for Medical Imaging Analysis: An Empirical Study [13.972931873011914]
  Large language models (LLMs) have taken the spotlight in natural language processing. Visual language models (VLMs), such as LLaVA, Flamingo, or CLIP, have demonstrated impressive performance on various visio-linguistic tasks.
  arXiv  Detail & Related papers  (2024-02-21T23:01:38Z)
• Ophtha-LLaMA2: A Large Language Model for Ophthalmology [31.39653268440651]
  Large language models (LLMs) have achieved tremendous success in the field of Natural Language Processing (NLP) In this study, we build an LLM termed the "Ophtha-LLaMA2" specifically tailored for ophthalmic disease diagnosis. Inference test results show that even with a smaller fine-tuning dataset, Ophtha-LLaMA2 performs significantly better in ophthalmic diagnosis.
  arXiv  Detail & Related papers  (2023-12-08T08:43:46Z)
• XrayGPT: Chest Radiographs Summarization using Medical Vision-Language Models [60.437091462613544]
  We introduce XrayGPT, a novel conversational medical vision-language model. It can analyze and answer open-ended questions about chest radiographs. We generate 217k interactive and high-quality summaries from free-text radiology reports.
  arXiv  Detail & Related papers  (2023-06-13T17:59:59Z)
• ChatCAD: Interactive Computer-Aided Diagnosis on Medical Image using Large Language Models [53.73049253535025]
  Large language models (LLMs) have recently demonstrated their potential in clinical applications. This paper presents a method for integrating LLMs into medical-image CAD networks. The goal is to merge the strengths of LLMs' medical domain knowledge and logical reasoning with the vision understanding capability of existing medical-image CAD models.
  arXiv  Detail & Related papers  (2023-02-14T18:54:06Z)

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.