Advancing Medical Radiograph Representation Learning: A Hybrid Pre-training Paradigm with Multilevel Semantic Granularity
<|reference_start|>Advancing Medical Radiograph Representation Learning: A Hybrid Pre-training Paradigm with Multilevel Semantic Granularity: 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.<|reference_end|>
@article{jiang2024advancing,
    title={Advancing Medical Radiograph Representation Learning: A Hybrid
  Pre-training Paradigm with Multilevel Semantic Granularity},
    author={Hanqi Jiang, Xixuan Hao, Yuzhou Huang, Chong Ma, Jiaxun Zhang, Yi Pan,
  and Ruimao Zhang},
    journal={ECCV 2024 Workshop},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.00448},
    primaryClass={cs.CV}
}