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| Content Provider | IET Digital Library |
|---|---|
| Author | Cui, Chongyang Fan, Shangchun Lei, Han Qu, Xiaolei Zheng, Dezhi |
| Abstract | In pathological diagnosis of breast cancer, there are problems such as shortage of pathologists, difficulties in sample labeling, and huge workload of manual diagnosis. Therefore, deep learning-based computer-assisted pathology analysis systems have been developed to diagnose breast cancer and have achieved impressive results. However, it is difficult to obtain a large number of training sets due to the scarcity of pathological images and the huge labeling costs. Therefore, the size of the training set should be planned before building the pathology computer-assisted breast cancer analysis system. Here, the authors present a study to determine the optimal size of the training data set needed to achieve high classification accuracy when developing a pathology computer-assisted breast cancer analysis system. The authors trained two kind of CNNs using six different sizes of training data set and then tested the resulting system with a total of 10,000 images. All images were acquired from the Camelyon17 challenge. Here, the authors propose a scheme for determining the size of the training set and the size of the model in developing the pathology computer-assisted breast cancer analysis systems, which can be easily applied to develop systems for other different pathological images. |
| Starting Page | 8729 |
| Ending Page | 8732 |
| 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.2018.9093 |
| 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 | Biology And Medical Computing Breast Cancer Pathology Detection Cancer Computer Vision And Image Processing Technique Deep Learning-based Computer-assisted Pathology Analysis System Different Pathological Image Feature Extraction Image Classification Knowledge Engineering Technique Learning in AI Medical Image Processing Neural Computing Technique Neural Nets Optical, Image And Video Signal Processing Pathological Diagnosis Pathology Computer-assisted Breast Cancer Analysis System Patient Diagnosis Patient Diagnostic Method And Instrumentation Training Data Set Training Data Size Training Set |
| Content Type | Text |
| Resource Type | Article |
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