Abstract
Skin disease is one of the global burdens of disease, and affects around 30% to 70% individuals worldwide. Effective automatic diagnosis is indispensable for doctors and patients. Compared with dermoscopic imaging, using clinical images captured by a portable electronic device (e.g. a mobile phone) is more available and low-cost. However, the existing large clinical skin-disease image datasets do not have the spatial annotation information, thus posing challenges for localizing the skin-disease regions and learning detailed features. To address the problem, we propose the Interactive Attention Sampling Network (IASN) which automatically localizes the target skin-disease regions and highlight the regions into high resolution. Specifically, the top-K local peaks of the class activation maps are collected, which indicate the key clues of skin-disease images. Then the features of the local peaks are interacted with the features of the surrounding context. With the guidance of the interactive attention maps, the non-uniform sampled images are generated, which facilitate the model to learn more discriminative features. Experimental results demonstrate that the proposed IASN outperforms the state-of-the-art methods on the SD-198 benchmark dataset.
This work was supported by the Guangdong Basic and Applied Basic Research Foundation No. 2021A1515011867, the Taishan Young Scholars Program of Shandong Province, the Key Development Program for Basic Research of Shandong Province No. ZR2020ZD44, and the National Natural Science Foundation of China No. 61503084 and No. 61976123.
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Chen, X., Li, D., Zhang, Y., Jian, M. (2021). Interactive Attention Sampling Network for Clinical Skin Disease Image Classification. In: Ma, H., et al. Pattern Recognition and Computer Vision. PRCV 2021. Lecture Notes in Computer Science(), vol 13021. Springer, Cham. https://doi.org/10.1007/978-3-030-88010-1_33
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