Abstract
Deep learning has shown significant progress in medical image analysis tasks such as semantic segmentation. However, deep learning models typically require large amounts of annotated data to achieve high accuracy; often a limiting factor in medical applications where labeled data is scarce. Few-shot domain adaptation (FSDA) is one approach to address this problem. It adapts a model trained on a source domain to a target domain which includes a few labeled data. In this paper, we present an FSDA method adapting pre-trained models to the target domain via domain-mixed data in the self-training framework. Our network follows the traditional encoder-decoder structure, which consists of a Transformer encoder, a DeeplabV3+ decoder for segmentation tasks and an auxiliary decoder for boundary-supervised learning. Our approach fine-tunes the source-domain pre-trained model with a few labeled examples from the target domain and by including unlabeled target domain data as well. We evaluate our method on two commonly used publicly available datasets for optic disc/cup and polyp segmentation, and show that it outperforms other state-of-the-art FSDA methods with only 5 labeled examples in the target domain. Overall, our FSDA method shows promising results and has potential to be applied to other medical imaging tasks with limited labeled data in the target domain.
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Wang, Y., Pagnucco, M., Song, Y. (2024). Self-training with Domain-Mixed Data for Few-Shot Domain Adaptation in Medical Image Segmentation Tasks. In: Cao, X., Xu, X., Rekik, I., Cui, Z., Ouyang, X. (eds) Machine Learning in Medical Imaging. MLMI 2023. Lecture Notes in Computer Science, vol 14348. Springer, Cham. https://doi.org/10.1007/978-3-031-45673-2_30
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