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Masked autoencoders with generalizable self-distillation for skin lesion segmentation
Yichen Zhi1, Hongxia Bie2, Jiali Wang1
1Department of Intelligent Media Computing Center, School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, 100876, People's Republic of China.
Medical & Biological Engineering & Computing
|April 23, 2024
Summary
This study introduces TEDMAE, a novel self-supervised learning method for skin lesion segmentation. TEDMAE enhances feature learning using a teacher-student architecture and data augmentation, improving accuracy in skin cancer analysis.
Area of Science:
- Medical Imaging
- Computer Vision
- Dermatology
Background:
- Accurate skin lesion segmentation is crucial for skin cancer analysis.
- Self-supervised learning, particularly Masked Autoencoders (MAE), shows promise in medical imaging by learning from unlabeled data.
- Existing methods may struggle to capture both global structure and local details in dermoscopy images.
Purpose of the Study:
- To introduce TEDMAE, a teacher-student architecture with self-distillation for enhanced skin lesion image segmentation.
- To improve the learning of global and local features in dermoscopy images using self-supervised learning.
- To enhance the generalizability of pre-trained models for skin lesion segmentation on unseen data.
Main Methods:
- Developed a Teacher-Student architecture (TEDMAE) incorporating self-distillation for holistic feature learning.
- Implemented Exterior Conversion Augmentation (EC) using convolutional kernels and linear interpolation for texture and intensity transformation.
- Utilized Dynamic Feature Generation (DF) with a nonlinear attention mechanism for improved feature expressiveness and merging.
Main Results:
- TEDMAE outperformed existing methods on ISIC2019, ISIC2017, and PH2 datasets.
- Achieved optimal segmentation performance on ISIC2017 (Dice: 82.1%) and PH2 (Dice: 91.2%).
- Demonstrated superior Jaccard (up to 84.5%) and HD95% (as low as 8.9%) scores, indicating strong generalization.
Conclusions:
- TEDMAE effectively learns comprehensive global and local features for skin lesion segmentation.
- The proposed EC and DF strategies significantly enhance model generalizability.
- TEDMAE represents a significant advancement in self-supervised learning for medical image analysis, particularly in dermatology.
