Complex Mixer for MedMNIST Classification Decathlon

• URL: http://arxiv.org/abs/2304.10054v1
• Date: Thu, 20 Apr 2023 02:34:36 GMT
• Title: Complex Mixer for MedMNIST Classification Decathlon
• Authors: Zhuoran Zheng and Xiuyi Jia
• Abstract summary: We develop a Complex Mixer (C-Mixer) with a pre-training framework to alleviate the problem of insufficient information and uncertainty in the label space. Our method shows surprising potential on both the standard MedMNIST (v2) dataset and the customized weakly supervised datasets.
• Score: 12.402054374952485
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: With the development of the medical image field, researchers seek to develop a class of datasets to block the need for medical knowledge, such as \text{MedMNIST} (v2). MedMNIST (v2) includes a large number of small-sized (28 $\times$ 28 or 28 $\times$ 28 $\times$ 28) medical samples and the corresponding expert annotations (class label). The existing baseline model (Google AutoML Vision, ResNet-50+3D) can reach an average accuracy of over 70\% on MedMNIST (v2) datasets, which is comparable to the performance of expert decision-making. Nevertheless, we note that there are two insurmountable obstacles to modeling on MedMNIST (v2): 1) the raw images are cropped to low scales may cause effective recognition information to be dropped and the classifier to have difficulty in tracing accurate decision boundaries; 2) the labelers' subjective insight may cause many uncertainties in the label space. To address these issues, we develop a Complex Mixer (C-Mixer) with a pre-training framework to alleviate the problem of insufficient information and uncertainty in the label space by introducing an incentive imaginary matrix and a self-supervised scheme with random masking. Our method (incentive learning and self-supervised learning with masking) shows surprising potential on both the standard MedMNIST (v2) dataset, the customized weakly supervised datasets, and other image enhancement tasks.

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