TY - GEN
T1 - Medical Image Classification Using Self-Supervised Learning-Based Masked Autoencoder
AU - Fan, Zong
AU - Wang, Zhimin
AU - Gong, Ping
AU - Lee, Christine U.
AU - Tang, Shanshan
AU - Zhang, Xiaohui
AU - Hao, Yao
AU - Zhang, Zhongwei
AU - Song, Pengfei
AU - Chen, Shigao
AU - Li, Hua
N1 - Publisher Copyright:
© 2024 SPIE.
PY - 2024
Y1 - 2024
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85193506998&partnerID=8YFLogxK
U2 - 10.1117/12.3006938
DO - 10.1117/12.3006938
M3 - Conference contribution
AN - SCOPUS:85193506998
T3 - Progress in Biomedical Optics and Imaging - Proceedings of SPIE
BT - Medical Imaging 2024
A2 - Colliot, Olivier
A2 - Mitra, Jhimli
PB - SPIE
T2 - Medical Imaging 2024: Image Processing
Y2 - 19 February 2024 through 22 February 2024
ER -