Medical image classification using self-supervised learning-based masked autoencoder

Zong Fan; Zhimin Wang; Ping Gong; Christine U. Lee; Shanshan Tang; Xiaohui Zhang; Yao Hao; Zhongwei Zhang; Pengfei Song; Shigao Chen; Hua Li

doi:10.1117/12.3006938

2 April 2024 Medical image classification using self-supervised learning-based masked autoencoder

Zong Fan, Zhimin Wang, Ping Gong, Christine U. Lee, Shanshan Tang, Xiaohui Zhang, Yao Hao, Zhongwei Zhang, Pengfei Song, Shigao Chen, Hua Li

Author Affiliations +

Proceedings Volume 12926, Medical Imaging 2024: Image Processing; 129260G (2024) https://doi.org/10.1117/12.3006938
Event: SPIE Medical Imaging, 2024, San Diego, California, United States

Abstract

Accurate classification of medical images is crucial for disease diagnosis and treatment planning. Deep learning (DL) methods have gained increasing attention in this domain. However, DL-based classification methods encounter challenges due to the unique characteristics of medical image datasets, including limited amounts of labeled images and large image variations. Self-supervised learning (SSL) has emerged as a solution that learns informative representations from unlabeled data to alleviate the scarcity of labeled images and improve model performance. A recently proposed generative SSL method, masked autoencoder (MAE), has shown excellent capability in feature representation learning. The MAE model trained on unlabeled data can be easily tuned to improve the performance of various downstream classification models. In this paper, we performed a preliminary study to integrate MAE with the self-attention mechanism for tumor classification on breast ultrasound (BUS) data. Considering the speckle noise, image quality variations of BUS images, and varying tumor shapes and sizes, two revisions were adopted in using MAE for tumor classification. First, MAE’s patch size and masking ratio were adjusted to avoid missing information embedded in small lesions on BUS images. Second, attention maps were extracted to improve the interpretability of the model’s decision-making process. Experiments demonstrated the effectiveness and potential of the MAE-based classification model on small labeled datasets.

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(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Zong Fan, Zhimin Wang, Ping Gong, Christine U. Lee, Shanshan Tang, Xiaohui Zhang, Yao Hao, Zhongwei Zhang, Pengfei Song, Shigao Chen, and Hua Li "Medical image classification using self-supervised learning-based masked autoencoder", Proc. SPIE 12926, Medical Imaging 2024: Image Processing, 129260G (2 April 2024); https://doi.org/10.1117/12.3006938

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