  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.
