RGMIM: Region-Guided Masked Image Modeling for Learning Meaningful Representations from X-Ray Images

• URL: http://arxiv.org/abs/2211.00313v5
• Date: Sat, 17 Aug 2024 14:59:56 GMT
• Title: RGMIM: Region-Guided Masked Image Modeling for Learning Meaningful Representations from X-Ray Images
• Authors: Guang Li, Ren Togo, Takahiro Ogawa, Miki Haseyama,
• Abstract summary: We propose a novel method called region-guided masked image modeling (RGMIM) for learning meaningful representations from X-ray images. RGMIM significantly improved performance in small data volumes, such as 5% and 10% of the training set compared to other methods.
• Score: 49.24576562557866
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this study, we propose a novel method called region-guided masked image modeling (RGMIM) for learning meaningful representations from X-ray images. Our method adopts a new masking strategy that utilizes organ mask information to identify valid regions for learning more meaningful representations. We conduct quantitative evaluations on an open lung X-ray image dataset as well as masking ratio hyperparameter studies. When using the entire training set, RGMIM outperformed other comparable methods, achieving a 0.962 lung disease detection accuracy. Specifically, RGMIM significantly improved performance in small data volumes, such as 5% and 10% of the training set compared to other methods. RGMIM can mask more valid regions, facilitating the learning of discriminative representations and the subsequent high-accuracy lung disease detection. RGMIM outperforms other state-of-the-art self-supervised learning methods in experiments, particularly when limited training data is used.

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