A dynamic few-shot learning framework for medical image stream mining based on self-training

Abstract

Few-shot semantic segmentation (FSS) has been widely used in the field of information medicine and intelligent diagnosis. Due to the high cost of medical data collection and the privacy protection of patients, labeled medical images are difficult to obtain. Compared with other semantic segmentation dataset which can be automatically generated in a large scale, the medical image data tend to be continually generated. Most of the existing FSS techniques require abundant annotated semantic classes for pre-training and cannot deal with its dynamic nature of medical data stream. To deal with this issue, we propose a dynamic few-shot learning framework for medical semantic segmentation, which can fully utilize the features of newly-collected/generated data stream. We introduce a new pseudo-label generation strategy for continuously generating pseudo-labels and avoiding model collapse during self-training. Furthermore, an efficient consistency regularization strategy is proposed to fully utilize the limited data. The proposed framework is iteratively trained on three tasks: abdominal organ segmentation for CT and MRI, and cardiac segmentation for MRI. Experiments results demonstrate significant performance gain on medical data stream mining compared with the baseline method.