Exploring Intrinsic Properties of Medical Images for Self-Supervised Binary Semantic Segmentation
This addresses the challenge of scarce labeled data for medical image segmentation, offering a domain-specific incremental improvement.
The paper tackled the problem of limited labeled data in medical image segmentation by introducing MedSASS, a self-supervised framework, which outperformed CNN-based methods by 3.83% and matched ViT-based methods, with end-to-end training improving performance by 14.4% for CNNs and 6% for ViTs.
Recent advancements in self-supervised learning have unlocked the potential to harness unlabeled data for auxiliary tasks, facilitating the learning of beneficial priors. This has been particularly advantageous in fields like medical image analysis, where labeled data are scarce. Although effective for classification tasks, this methodology has shown limitations in more complex applications, such as medical image segmentation. In this paper, we introduce Medical imaging Enhanced with Dynamic Self-Adaptive Semantic Segmentation (MedSASS), a dedicated self-supervised framework tailored for medical image segmentation. We evaluate MedSASS against existing state-of-the-art methods across four diverse medical datasets, showcasing its superiority. MedSASS outperforms existing CNN-based self-supervised methods by 3.83% and matches the performance of ViT-based methods. Furthermore, when MedSASS is trained end-to-end, covering both encoder and decoder, it demonstrates significant improvements of 14.4% for CNNs and 6% for ViT-based architectures compared to existing state-of-the-art self-supervised strategies.