Exploring the Versatility of Zero-Shot CLIP for Interstitial Lung Disease Classification

• URL: http://arxiv.org/abs/2306.01111v2
• Date: Tue, 12 Sep 2023 20:58:35 GMT
• Title: Exploring the Versatility of Zero-Shot CLIP for Interstitial Lung Disease Classification
• Authors: Cara Van Uden and Christian Bluethgen and Maayane Attias and Malgorzata Polacin and Haiwei Henry Guo and Neha Simha and Rishi Raj and Curtis Langlotz
• Abstract summary: We propose a machine learning approach that utilizes CLIP, a multimodal (image and text) self-supervised model, for ILD classification. We extensively integrate zero-shot CLIP throughout our workflow, starting from the initial extraction of image patches from volumetric CT scans. We achieve strong zero-shot ILD classification results, including an AUROC of 0.893, without the need for any labeled training data.
• Score: 0.36646002427839136
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Interstitial lung diseases (ILD) present diagnostic challenges due to their varied manifestations and overlapping imaging features. To address this, we propose a machine learning approach that utilizes CLIP, a multimodal (image and text) self-supervised model, for ILD classification. We extensively integrate zero-shot CLIP throughout our workflow, starting from the initial extraction of image patches from volumetric CT scans and proceeding to ILD classification using "patch montages". Furthermore, we investigate how domain adaptive pretraining (DAPT) CLIP with task-specific images (CT "patch montages" extracted with ILD-specific prompts for CLIP) and/or text (lung-specific sections of radiology reports) affects downstream ILD classification performance. By leveraging CLIP-extracted "patch montages" and DAPT, we achieve strong zero-shot ILD classification results, including an AUROC of 0.893, without the need for any labeled training data. This work highlights the versatility and potential of multimodal models like CLIP for medical image classification tasks where labeled data is scarce.

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