In recent years, deep learning has become a breakthrough technique in assisting medical image diagnosis. Supervised learning using convolutional neural networks (CNN) provides state-of-the-art performance and has served as a benchmark for various medical image segmentation and classification. However, supervised learning deeply relies on large-scale annotated data, which is expensive, time-consuming, and even impractical to acquire in medical imaging applications. Active Learning (AL) methods have been widely applied in natural image classification tasks to reduce annotation costs by selecting more valuable examples from the unlabeled data pool. However, their application in medical image segmentation tasks is limited, and there is currently no effective and universal AL-based method specifically designed for 3D medical image segmentation. To address this limitation, we propose an AL-based method that can be simultaneously applied to 2D medical image classification, segmentation, and 3D medical image segmentation tasks. We extensively validated our proposed active learning method on three publicly available and challenging medical image datasets, Kvasir Dataset, COVID-19 Infection Segmentation Dataset, and BraTS2019 Dataset. The experimental results demonstrate that our PCDAL can achieve significantly improved performance with fewer annotations in 2D classification and segmentation and 3D segmentation tasks. The codes of this study are available at https://github.com/ortonwang/PCDAL.

翻译：近年来，深度学习已成为辅助医学图像诊断的突破性技术。基于卷积神经网络的监督学习提供了最先进的性能，并已成为各类医学图像分割与分类的基准方法。然而，监督学习高度依赖大规模标注数据，这在医学成像应用中成本高昂、耗时甚至难以实现。主动学习方法已广泛应用于自然图像分类任务，通过从未标注数据池中挑选更有价值的样本来降低标注成本。但其在医学图像分割任务中的应用仍有限，目前尚缺乏针对3D医学图像分割的有效且通用的主动学习方法。为解决这一局限，我们提出了一种可同时应用于2D医学图像分类、分割及3D医学图像分割任务的主动学习方法。我们在三个公开且具有挑战性的医学图像数据集——Kvasir数据集、COVID-19感染分割数据集和BraTS2019数据集上，对所提出的主动学习方法进行了广泛验证。实验结果表明，我们的PCDAL方法在2D分类与分割及3D分割任务中，能以更少的标注量实现显著提升的性能。本研究的代码可在https://github.com/ortonwang/PCDAL获取。