PCDAL: A Perturbation Consistency-Driven Active Learning Approach for Medical Image Segmentation and Classification

TL;DR

This paper introduces PCDAL, an active learning approach tailored for medical image segmentation and classification, which reduces annotation needs while maintaining high performance across 2D and 3D medical imaging tasks.

Contribution

The paper presents a novel active learning method specifically designed for 3D medical image segmentation, applicable to both classification and segmentation tasks, validated on multiple datasets.

Findings

Achieves high accuracy with fewer annotations

Effective for 2D and 3D medical image tasks

Validated on three challenging datasets

Abstract

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.