Adaptive batch-fusion self-supervised learning for ultrasound image pretraining.
Authors
Affiliations (6)
Affiliations (6)
- School of Computer Science and Software Engineering, Shenzhen University, Shenzhen, Guangdong Province, China; College of Medicine, Huaqiao University, Quanzhou, Fujian Province, China.
- Department of Ultrasound, Quanzhou First Hospital Affiliated to Fujian Medical University, Quanzhou, Fujian Province, China.
- Department of Ultrasound, The Second Hospital of Quanzhou Affiliated to Fujian Medical University, Quanzhou, Fujian Province, China.
- College of Engineering, Huaqiao University, Quanzhou, Fujian Province, China.
- College of Medicine, Huaqiao University, Quanzhou, Fujian Province, China; College of Engineering, Huaqiao University, Quanzhou, Fujian Province, China.
- Department of Ultrasound, The Second Hospital of Quanzhou Affiliated to Fujian Medical University, Quanzhou, Fujian Province, China. Electronic address: [email protected].
Abstract
Medical self-supervised learning eliminates the reliance on labels, making feature extraction simple and efficient. The intricate design of pretext tasks in single-modal self-supervised analysis presents challenges, however, compounded by an excessive dependency on data augmentation, leading to a bottleneck in medical self-supervised learning research. Consequently, this paper reanalyzes the feature learnability introduced by data augmentation strategies in medical image self-supervised learning. We introduce an adaptive self-supervised learning data augmentation method from the perspective of batch fusion. Moreover, we propose a conv embedding block for learning the incremental representation between these batches. We tested 5 fused data tasks proposed by previous researchers and it achieved a linear classification protocol accuracy of 94.25% with only 150 self-supervised feature training in Vision Transformer(ViT), which is the best among the same methods. With a detailed ablation study on previous augmentation strategies, the results indicate that the proposed medical data augmentation strategy in this paper effectively represents ultrasound data features in the self-supervised learning process. The code and weights could be found at here.