PCDAL: A Perturbation Consistency-Driven Active Learning Approach for
  Medical Image Segmentation and Classification
Tao Wang Xinlin Zhang Yuanbo Zhou Junlin Lan Tao Tan Min Du Qinquan Gao Tong Tong
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.