Improving DFU Image Classification by an Adaptive Augmentation Pool and Voting with Expertise
Xin Wu, Pin Xu, Haoyuan Chen, Jianping Yin, Kuan Li · 2023
Medical images are inseparable from data augmentation due to the lack of labeled data. However, several existing effective augments for natural images may not be suitable for medical images. This paper proposes an adaptive framework to construct a suitable augmentation pool for Diabetic Foot Ulcers medical images. Additionally, we use ensemble learning to enhance the model’s output. Instead of commonly used plurality voting, we propose a strategy named “voting with expertise” which prioritizes prediction with adequately reliable value. Experimental results show the effectiveness of the proposed methods and we won second place by combining the above two improvements in the ongoing open challenge-DFUC2021 Challenge.