FSC-CLLDA: few-shot medical image classification based on contrastive learning and LDA
Qinglei Guo, Wenjing Zhang, Tao Luo, Jianfeng Li · 2024
Deep learning (DL) techniques have been widely applied in medical image analysis. In particular, the DL-based medical image classification has been adequately investigated on large-size annotated datasets. However, it is cost-expensive to collect a large amount of high-quality and large-scale annotated medical images. Our proposal is addressing this problem by a few-shot medical image classification method that uses contrastive learning and linear discriminant analysis (FSCCLLDA). A well-performing encoder is pre-trained using contrastive learning to extract more extensive semantic information that is unrelated to the label. Moreover, the features are transformed into low-dimensional space using linear discriminant analysis (LDA). The transformed features are similar within each class and discriminatory among classes. Experiments on ISIC2018 and BreakHis datasets show that the proposed FSC-CLLDA algorithm outperforms the compared baselines in accuracy.