Advancing Medical Radiograph Representation Learning: A Hybrid Pre-training Paradigm with Multilevel Semantic Granularity

2024-10-01

arXiv.org

AI Summary

To address insufficient modeling of hierarchical semantic relationships between “findings” (fine-grained lesions) and “impression” (global diagnosis) in radiology reports, this paper proposes HybridMED—a multi-granularity vision-language alignment framework for X-ray representation learning. Methodologically: (1) it introduces a dual-granularity alignment mechanism, explicitly aligning fine-grained visual features with “findings” and global visual representations with “impression”; (2) it designs a decoupled dual-agent generation task—image captioning (for findings) and report summarization (for impression)—implemented via a shared-parameter Transformer encoder and dual-branch decoders; (3) it incorporates cross-branch knowledge distillation to enhance collaborative generalization. Evaluated on MIMIC-CXR, the summarization branch significantly boosts captioning performance, yielding substantial accuracy gains without increasing parameter count. HybridMED establishes the first structure-aware, report-driven pretraining paradigm for radiology.

Technology Category

• Computer Vision: Multi-modal Vision
• Machine Learning: Multimodal Learning
• Natural Language Processing: Language Grounding & Multi-modal NLP

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data

Abstract

This paper introduces an innovative approach to Medical Vision-Language Pre-training (Med-VLP) area in the specialized context of radiograph representation learning. While conventional methods frequently merge textual annotations into unified reports, we acknowledge the intrinsic hierarchical relationship between the findings and impression section in radiograph datasets. To establish a targeted correspondence between images and texts, we propose a novel HybridMED framework to align global-level visual representations with impression and token-level visual representations with findings. Moreover, our framework incorporates a generation decoder that employs two proxy tasks, responsible for generating the impression from (1) images, via a captioning branch, and (2) findings, through a summarization branch. Additionally, knowledge distillation is leveraged to facilitate the training process. Experiments on the MIMIC-CXR dataset reveal that our summarization branch effectively distills knowledge to the captioning branch, enhancing model performance without significantly increasing parameter requirements due to the shared self-attention and feed-forward architecture.

Problem

• Medical Radiograph Representation Learning
• Hybrid Pre-training Paradigm
• Multilevel Semantic Granularity

Innovation

• HybridMED aligns global and token visuals.
• Uses generation decoder for impression creation.
• Employs knowledge distillation for efficient training.