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| Content Provider | IET Digital Library |
|---|---|
| Author | Lei, Han Wang, Shuai Zheng, Dezhi Qu, Xiaolei Fan, Shangchun Cui, Chongyang |
| Abstract | Image classification is a fundamental task in image analysis. Recent advances in deep learning have achieved promising results on many image classification benchmarks. However, in some particular tasks, especially in biomedical image analysis, preparing a large number of labelled images for the model's training is costly and unpractical. In this study, the authors aim to address the following questions: With limited effort (e.g. time, cost and manpower) for labelling, what instances should be chosen to annotate and how to train to model using limited annotated data. For that, they present an active learning algorithm combining with data balancing, making the model (e.g. convolutional neural network) fine-tuned continuously and incrementally to reduce the effort of labelling and making model's training process more robust and efficient in both binary and multi-class classification with high-performance. They have evaluated the authors’ method of both binary natural dataset and three classes biomedical dataset, demonstrating that active learning with data balancing could help models’ training more robust and broaden active learning's field to multi classification and more application scenarios. More significantly, their experiments showed that at least a half of effort in labelling could be saved for satisfied performance by their method. |
| Starting Page | 8650 |
| Ending Page | 8653 |
| Page Count | 4 |
| Volume Number | 2019 |
| e-ISSN | 20513305 |
| Issue Number | Issue 23, Dec (2019) |
| Alternate Webpage(s) | https://digital-library.theiet.org/content/journals/joe/2019/23 |
| Alternate Webpage(s) | https://digital-library.theiet.org/content/journals/10.1049/joe.2018.9076 |
| Journal | The Journal of Engineering |
| Publisher | The Institution of Engineering and Technology |
| Publisher Date | 2019-03-25 |
| Access Restriction | Open |
| Rights License | Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/) |
| Subject Keyword | Annotated Data Binary Natural Dataset Biomedical Dataset Biomedical Image Analysis Convolutional Neural Network Data Balancing Data Handling Technique Deep Learning Image Classification Benchmarks Improved Active Learning Knowledge Engineering Technique Labelled Image Learning in AI Multiclass Classification Neural Computing Technique Neural Nets Pattern Classification |
| Content Type | Text |
| Resource Type | Article |
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