Class-Specific Distribution Alignment for Semi-Supervised Medical Image
  Classification
Zhongzheng Huang Jiawei Wu Tao Wang Zuoyong Li Anastasia Ioannou
Despite the success of deep neural networks in medical image classification,
the problem remains challenging as data annotation is time-consuming, and the
class distribution is imbalanced due to the relative scarcity of diseases. To
address this problem, we propose Class-Specific Distribution Alignment (CSDA),
a semi-supervised learning framework based on self-training that is suitable to
learn from highly imbalanced datasets. Specifically, we first provide a new
perspective to distribution alignment by considering the process as a change of
basis in the vector space spanned by marginal predictions, and then derive CSDA
to capture class-dependent marginal predictions on both labeled and unlabeled
data, in order to avoid the bias towards majority classes. Furthermore, we
propose a Variable Condition Queue (VCQ) module to maintain a proportionately
balanced number of unlabeled samples for each class. Experiments on three
public datasets HAM10000, CheXpert and Kvasir show that our method provides
competitive performance on semi-supervised skin disease, thoracic disease, and
endoscopic image classification tasks.