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OphGLM

The first ophthalmology Large Language-and-Vision Assistant based on Instructions and Dialogue

Table of content

• Motivation
• Modules
• Dataset
• Building Process
• News
• Reference

Motivation

OphGLM aims to enhance ophthalmic diagnostics by integrating visual and language models, improving human-computer interaction and clinical applicability. With the introduction of the FundusTuning-CN dataset, we hope to demonstrate promising advancements in fundus disease classification and interactive capabilities, paving the way for future developments in this field.

Modules

Constructing a fine-tuning dataset suitable for large language models in specific diseases from both basic knowledge and dialogue perspectives:

The illustration of Dynamic Label Pairing Strategy:

Basic LLM Model and Pre-trained Model:

ChatGLM-6B

Dataset

We have provided some available data in this source code, including: Ophthalmology historical doctor-patient dialogue from year 2010 to 2020 & Fine-tunning data sample in JSON

For building a fine-tuning dataset for LLMs targeting specific diseases, we recommend data collection from two aspects: foundational background knowledge and doctor-patient dialogues, from a clinical application perspective. The potential difficulty here lies in the fact that for specific diseases, especially rare diseases, doctor-patient dialogue data is very scarce.

Process

Step1: Constructing the Classification Model Leverage the ODIR5K Fundus Image Dataset

• Selected images for Diabetic Retinopathy (DR), Age-related Macular Degeneration (AMD), Glaucoma, Myopia, and Cataracts from the ODIR5K dataset. Employing ConvNext as Image Encoder
• Used ConvNext for image encoding, pretraining on a multi-disease classification task.

Link: ODIR5K

Step2: Collecting and Building LLM Fine-tunning Datasets

Fundus Instruction Set

• Gathered information from web data and knowledge graphs, categorized into five subsets:
  • Visual Diagnostic Instructions
  • Causes and Symptoms
  • Diagnosis and Examination
  • Treatment and Prevention
  • Prognosis and Lifestyle Fundus Conversation Set
• Assembled fundus-related conversations, covering both rich and limited ophthalmic knowledge.

Step3: OphGLM Architecture

Components

• Includes an Image Encoder, Text Encoder, Fusion Module, and a Large Language Model (LLM). Encoders and LLM Details
• Used BERT as the text encoder, ConvNext as the image encoder, and ChatGLM-6B as the LLM. OphGLM Fine-tuning Process Pretraining the Image Encoder
• Pretrained the image encoder on a multi-disease classification task. Tuning the Fusion Module
• Trained the fusion module on a visual question-answering task, restricting updates to this module. Fine-tuning the LLM
• Applied supervised fine-tuning to the LLM using image-text and plain text data to enhance multimodal comprehension.

News

2024.9.30 The core code and sample data have been uploaded! :triangular_flag_on_post: