Abstract
As an emerging early diagnostic technology for gastrointestinal diseases, confocal laser endomicroscopy lacks large-scale perfect annotated data, leading to a major challenge in learning discriminative semantic features. So, how should we learn representations without labels or a few labels? In this paper, we proposed a feature-level MixSiam method based on the traditional Siamese network that learns the discriminative features of probe-based confocal laser endomicroscopy (pCLE) images for gastrointestinal (GI) tumor classification. The proposed method is divided into two stages: self-supervised learning (SSL) and few-shot learning (FS). First, in the self-supervised learning stage, the novel feature-level-based feature mixing approach introduced more task-relevant information via regularization, facilitating the traditional Siamese structure can adapt to the large intra-class variance of the pCLE dataset. Then, in the few-shot learning stage, we adopted the pre-trained model obtained through self-supervised learning as the base learner in the few-shot learning pipeline, enabling the feature extractor to learn richer and more transferable visual representations for rapid generalization to other pCLE classification tasks when labeled data are limited. On two disjoint pCLE gastrointestinal image datasets, the proposed method is evaluated. With the linear evaluation protocol, feature-level MixSiam outperforms the baseline by 6% (Top-1) and the supervised model by 2% (Top1), which demonstrates the effectiveness of the proposed feature-level-based feature mixing method. Furthermore, the proposed method outperforms the previous baseline method for the few-shot classification task, which can help improve the classification of pCLE images lacking large-scale annotated data for different stages of tumor development.