191 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
Multi-criteria-based active learning for named entity recognition
Master'sMASTER OF SCIENC
Maturity-Aware Active Learning for Semantic Segmentation with Hierarchically-Adaptive Sample Assessment
Active Learning (AL) for semantic segmentation is challenging due to heavy
class imbalance and different ways of defining "sample" (pixels, areas, etc.),
leaving the interpretation of the data distribution ambiguous. We propose
"Maturity-Aware Distribution Breakdown-based Active Learning'' (MADBAL), an AL
method that benefits from a hierarchical approach to define a multiview data
distribution, which takes into account the different "sample" definitions
jointly, hence able to select the most impactful segmentation pixels with
comprehensive understanding. MADBAL also features a novel uncertainty
formulation, where AL supporting modules are included to sense the features'
maturity whose weighted influence continuously contributes to the uncertainty
detection. In this way, MADBAL makes significant performance leaps even in the
early AL stage, hence reducing the training burden significantly. It
outperforms state-of-the-art methods on Cityscapes and PASCAL VOC datasets as
verified in our extensive experiments.Comment: Accepted to the 34th British Machine Vision Conference (BMVC 2023
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