Consecutive knowledge meta-adaptation learning for unsupervised medical diagnosis

Abstract

Deep learning-based computer-aided diagnosis has garnered significant attention in both academic research and clinical applications. Due to the challenges in collecting well-labeled clinical data, unsupervised domain adaptation methods have become widely used in medical image analysis. However, existing unsupervised domain adaptation methods in the clinic field fail to adapt to scenarios where new, unlabeled target data continually arrive in an online manner. To tackle this real-world clinical challenge, a meta-adaptation framework is innovatively developed, termed consecutive lesion knowledge meta-adaptation, primarily comprising semantic adaptation and representation adaptation phases. Specifically, the semantic knowledge learned from the source lesion domain will be transferred to the consecutive target lesion in the semantic adaptation phase. To further align the transferable representation knowledge across the source and multiple target lesion domains, the feature-extractor is optimized in the representation adaptation phase. Furthermore, to mitigate catastrophic forgetting during continuous unsupervised domain adaptation, a domain-quantizer is designed to preserve utility knowledge from previously learned target lesion domains. Moreover, as semantic knowledge is continuously evolving and artificially designed kernels struggle to model such complex distributions, a self-adaptive kernel is designed to flexibly measure the shift between the source and consecutive target lesion domains during the semantic adaptation phase. Experimental results on medical image classification tasks demonstrate the effectiveness of the proposed method.