3,870 research outputs found
A comprehensive survey on deep active learning and its applications in medical image analysis
Deep learning has achieved widespread success in medical image analysis,
leading to an increasing demand for large-scale expert-annotated medical image
datasets. Yet, the high cost of annotating medical images severely hampers the
development of deep learning in this field. To reduce annotation costs, active
learning aims to select the most informative samples for annotation and train
high-performance models with as few labeled samples as possible. In this
survey, we review the core methods of active learning, including the evaluation
of informativeness and sampling strategy. For the first time, we provide a
detailed summary of the integration of active learning with other
label-efficient techniques, such as semi-supervised, self-supervised learning,
and so on. Additionally, we also highlight active learning works that are
specifically tailored to medical image analysis. In the end, we offer our
perspectives on the future trends and challenges of active learning and its
applications in medical image analysis.Comment: Paper List on Github:
https://github.com/LightersWang/Awesome-Active-Learning-for-Medical-Image-Analysi
- …