DeViDe: Faceted medical knowledge for improved medical vision-language pre-training

• URL: http://arxiv.org/abs/2404.03618v1
• Date: Thu, 4 Apr 2024 17:40:06 GMT
• Title: DeViDe: Faceted medical knowledge for improved medical vision-language pre-training
• Authors: Haozhe Luo, Ziyu Zhou, Corentin Royer, Anjany Sekuboyina, Bjoern Menze,
• Abstract summary: Vision-language pre-training for chest X-rays has made significant strides, primarily by utilizing paired radiographs and radiology reports. We propose DeViDe, a transformer-based method that leverages radiographic descriptions from the open web. DeViDe incorporates three key features for knowledge-augmented vision language alignment: First, a large-language model-based augmentation is employed to homogenise medical knowledge from diverse sources. In zero-shot settings, DeViDe performs comparably to fully supervised models on external datasets and achieves state-of-the-art results on three large-scale datasets.
• Score: 1.6567372257085946
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Vision-language pre-training for chest X-rays has made significant strides, primarily by utilizing paired radiographs and radiology reports. However, existing approaches often face challenges in encoding medical knowledge effectively. While radiology reports provide insights into the current disease manifestation, medical definitions (as used by contemporary methods) tend to be overly abstract, creating a gap in knowledge. To address this, we propose DeViDe, a novel transformer-based method that leverages radiographic descriptions from the open web. These descriptions outline general visual characteristics of diseases in radiographs, and when combined with abstract definitions and radiology reports, provide a holistic snapshot of knowledge. DeViDe incorporates three key features for knowledge-augmented vision language alignment: First, a large-language model-based augmentation is employed to homogenise medical knowledge from diverse sources. Second, this knowledge is aligned with image information at various levels of granularity. Third, a novel projection layer is proposed to handle the complexity of aligning each image with multiple descriptions arising in a multi-label setting. In zero-shot settings, DeViDe performs comparably to fully supervised models on external datasets and achieves state-of-the-art results on three large-scale datasets. Additionally, fine-tuning DeViDe on four downstream tasks and six segmentation tasks showcases its superior performance across data from diverse distributions.

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