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Rethinking deep active learning for medical image segmentation: A diffusion and angle-based framework
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DOI：10.1016/j.bspc.2024.106493.png)
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摘要
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Semantic segmentation based on deep learning typically necessitates a substantial number of finely annotated samples for effective model training. However, acquiring such annotations can be prohibitively expensive and challenging, particularly within the domain of medical image segmentation. Active learning (AL) offers a solution by enabling the selection of the most informative samples for labeling, thereby enhancing annotation efficiency and reducing associated costs. Nevertheless, most existing AL methods operate within an iterative paradigm, which is time-consuming and resource -intensive, thus limiting their practicality in medical applications. Recently, a more viable alternative called the one-shot AL paradigm has emerged, which involves a single interaction with experts and has demonstrated comparable performance to iterative algorithms in image segmentation tasks. In this paper, we propose DifABAL: a novel angle -based one-shot AL framework utilizing diffusion autoencoders for medical image segmentation. Specifically, we leverage diffusion model -based autoencoders to extract features from unlabeled samples and introduce a novel parameter -robust angle -based query strategy for selecting representative samples in the feature space. Extensive experiments conducted on three large publicly available datasets comprising pathology images, chest X-ray images, and dermatology images demonstrate the remarkable performance of our framework. The source code is available at https://github.com/miccaiif/DifABAL.
Keyword:
Active learning
Medical image segmentation
Data mining
期刊
IF:
4.9
论文数:
1.0W
被引数:
2.4W
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Active, continual fine tuning of convolutional neural networks for reducing annotation efforts.png)
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