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MedThink: Inducing Medical Large-scale Visual Language Models to Hallucinate Less by Thinking More

2024-06-18 14:20:46
Yue Jiang, Jiawei Chen, Dingkang Yang, Mingcheng Li, Shunli Wang, Tong Wu, Ke Li, Lihua Zhang


When Large Vision Language Models (LVLMs) are applied to multimodal medical generative tasks, they suffer from significant model hallucination issues. This severely impairs the model's generative accuracy, making it challenging for LVLMs to be implemented in real-world medical scenarios to assist doctors in diagnosis. Enhancing the training data for downstream medical generative tasks is an effective way to address model hallucination. Moreover, the limited availability of training data in the medical field and privacy concerns greatly hinder the model's accuracy and generalization capabilities. In this paper, we introduce a method that mimics human cognitive processes to construct fine-grained instruction pairs and apply the concept of chain-of-thought (CoT) from inference scenarios to training scenarios, thereby proposing a method called MedThink. Our experiments on various LVLMs demonstrate that our novel data construction method tailored for the medical domain significantly improves the model's performance in medical image report generation tasks and substantially mitigates the hallucinations. All resources of this work will be released soon.

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3D Action Action_Localization Action_Recognition Activity Adversarial Agent Attention Autonomous Bert Boundary_Detection Caption Chat Classification CNN Compressive_Sensing Contour Contrastive_Learning Deep_Learning Denoising Detection Dialog Diffusion Drone Dynamic_Memory_Network Edge_Detection Embedding Embodied Emotion Enhancement Face Face_Detection Face_Recognition Facial_Landmark Few-Shot Gait_Recognition GAN Gaze_Estimation Gesture Gradient_Descent Handwriting Human_Parsing Image_Caption Image_Classification Image_Compression Image_Enhancement Image_Generation Image_Matting Image_Retrieval Inference Inpainting Intelligent_Chip Knowledge Knowledge_Graph Language_Model LLM Matching Medical Memory_Networks Multi_Modal Multi_Task NAS NMT Object_Detection Object_Tracking OCR Ontology Optical_Character Optical_Flow Optimization Person_Re-identification Point_Cloud Portrait_Generation Pose Pose_Estimation Prediction QA Quantitative Quantitative_Finance Quantization Re-identification Recognition Recommendation Reconstruction Regularization Reinforcement_Learning Relation Relation_Extraction Represenation Represenation_Learning Restoration Review RNN Robot Salient Scene_Classification Scene_Generation Scene_Parsing Scene_Text Segmentation Self-Supervised Semantic_Instance_Segmentation Semantic_Segmentation Semi_Global Semi_Supervised Sence_graph Sentiment Sentiment_Classification Sketch SLAM Sparse Speech Speech_Recognition Style_Transfer Summarization Super_Resolution Surveillance Survey Text_Classification Text_Generation Tracking Transfer_Learning Transformer Unsupervised Video_Caption Video_Classification Video_Indexing Video_Prediction Video_Retrieval Visual_Relation VQA Weakly_Supervised Zero-Shot