Cost-Effective Active Learning for Melanoma Segmentation
This work addresses the challenge of reducing labeling costs for medical image segmentation, which is incremental as it builds on existing active learning methods.
The authors tackled the problem of training a convolutional neural network for melanoma segmentation with limited labeled data by proposing a cost-effective active learning framework that uses dropout at test time for uncertainty estimation, achieving improved training performance.
We propose a novel Active Learning framework capable to train effectively a convolutional neural network for semantic segmentation of medical imaging, with a limited amount of training labeled data. Our contribution is a practical Cost-Effective Active Learning approach using dropout at test time as Monte Carlo sampling to model the pixel-wise uncertainty and to analyze the image information to improve the training performance. The source code of this project is available at https://marc-gorriz.github.io/CEAL-Medical-Image-Segmentation/ .