| Autor: |
Ju M; Biomedical Engineering Research Center, The Chongqing University of Posts and Telecommunications, Chongqing 400065, P.R.China., Li X; Biomedical Engineering Research Center, The Chongqing University of Posts and Telecommunications, Chongqing 400065, P.R.China., Li Z; Biomedical Engineering Research Center, The Chongqing University of Posts and Telecommunications, Chongqing 400065, P.R.China. |
| Jazyk: |
čínština |
| Zdroj: |
Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi [Sheng Wu Yi Xue Gong Cheng Xue Za Zhi] 2020 Jun 25; Vol. 37 (3), pp. 519-526. |
| DOI: |
10.7507/1001-5515.201909040 |
| Abstrakt: |
The number of white blood cells in the leucorrhea microscopic image can indicate the severity of vaginal inflammation. At present, the detection of white blood cells in leucorrhea mainly relies on manual microscopy by medical experts, which is time-consuming, expensive and error-prone. In recent years, some studies have proposed to implement intelligent detection of leucorrhea white blood cells based on deep learning technology. However, such methods usually require manual labeling of a large number of samples as training sets, and the labeling cost is high. Therefore, this study proposes the use of deep active learning algorithms to achieve intelligent detection of white blood cells in leucorrhea microscopic images. In the active learning framework, a small number of labeled samples were firstly used as the basic training set, and a faster region convolutional neural network (Faster R-CNN) training detection model was performed. Then the most valuable samples were automatically selected for manual annotation, and the training set and the corresponding detection model were iteratively updated, which made the performance of the model continue to increase. The experimental results show that the deep active learning technology can obtain higher detection accuracy under less manual labeling samples, and the average precision of white blood cell detection could reach 90.6%, which meets the requirements of clinical routine examination. |
| Databáze: |
MEDLINE |
| Externí odkaz: |
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