DiCoM -- Diverse Concept Modeling towards Enhancing Generalizability in Chest X-Ray Studies

• URL: http://arxiv.org/abs/2402.15534v2
• Date: Wed, 24 Jul 2024 14:39:53 GMT
• Title: DiCoM -- Diverse Concept Modeling towards Enhancing Generalizability in Chest X-Ray Studies
• Authors: Abhijeet Parida, Daniel Capellan-Martin, Sara Atito, Muhammad Awais, Maria J. Ledesma-Carbayo, Marius G. Linguraru, Syed Muhammad Anwar,
• Abstract summary: Chest X-Ray (CXR) is a widely used clinical imaging modality. Self-supervised pre-training has proven to outperform supervised pre-training in numerous downstream vision tasks. We introduce Diverse Concept Modeling (DiCoM), a novel self-supervised training paradigm.
• Score: 6.83819481805979
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Chest X-Ray (CXR) is a widely used clinical imaging modality and has a pivotal role in the diagnosis and prognosis of various lung and heart related conditions. Conventional automated clinical diagnostic tool design strategies relying on radiology reads and supervised learning, entail the cumbersome requirement of high quality annotated training data. To address this challenge, self-supervised pre-training has proven to outperform supervised pre-training in numerous downstream vision tasks, representing a significant breakthrough in the field. However, medical imaging pre-training significantly differs from pre-training with natural images (e.g., ImageNet) due to unique attributes of clinical images. In this context, we introduce Diverse Concept Modeling (DiCoM), a novel self-supervised training paradigm that leverages a student teacher framework for learning diverse concepts and hence effective representation of the CXR data. Hence, expanding beyond merely modeling a single primary label within an image, instead, effectively harnessing the information from all the concepts inherent in the CXR. The pre-trained model is subsequently fine-tuned to address diverse domain-specific tasks. Our proposed paradigm consistently demonstrates robust performance across multiple downstream tasks on multiple datasets, highlighting the success and generalizability of the pre-training strategy. To establish the efficacy of our methods we analyze both the power of learned representations and the speed of convergence (SoC) of our models. For diverse data and tasks, DiCoM is able to achieve in most cases better results compared to other state-of-the-art pre-training strategies. This when combined with the higher SoC and generalization capabilities positions DiCoM to be established as a foundation model for CXRs, a widely used imaging modality.

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