A Novel Method Based on GAN Using a Segmentation Module for Oligodendroglioma Pathological Image Generation
Digital pathology analysis using deep learning has been the subject of several studies. As with other medical data, pathological data are not easily obtained. Because deep learning-based image analysis requires large amounts of data, augmentation techniques are used to increase the size of pathologi...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-05-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/22/10/3960 |
| _version_ | 1797495622322880512 |
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| author | Juwon Kweon Jisang Yoo Seungjong Kim Jaesik Won Soonchul Kwon |
| author_facet | Juwon Kweon Jisang Yoo Seungjong Kim Jaesik Won Soonchul Kwon |
| author_sort | Juwon Kweon |
| collection | DOAJ |
| description | Digital pathology analysis using deep learning has been the subject of several studies. As with other medical data, pathological data are not easily obtained. Because deep learning-based image analysis requires large amounts of data, augmentation techniques are used to increase the size of pathological datasets. This study proposes a novel method for synthesizing brain tumor pathology data using a generative model. For image synthesis, we used embedding features extracted from a segmentation module in a general generative model. We also introduce a simple solution for training a segmentation model in an environment in which the masked label of the training dataset is not supplied. As a result of this experiment, the proposed method did not make great progress in quantitative metrics but showed improved results in the confusion rate of more than 70 subjects and the quality of the visual output. |
| first_indexed | 2024-03-10T01:52:17Z |
| format | Article |
| id | doaj.art-b8aa6adfc3f34a1b8add00b3ea637bd0 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T01:52:17Z |
| publishDate | 2022-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-b8aa6adfc3f34a1b8add00b3ea637bd02023-11-23T13:04:11ZengMDPI AGSensors1424-82202022-05-012210396010.3390/s22103960A Novel Method Based on GAN Using a Segmentation Module for Oligodendroglioma Pathological Image GenerationJuwon Kweon0Jisang Yoo1Seungjong Kim2Jaesik Won3Soonchul Kwon4Department of Electronic Engineering, Kwangwoon University, Seoul 01897, KoreaDepartment of Electronic Engineering, Kwangwoon University, Seoul 01897, KoreaMolpaxbio, Daejeon 34047, KoreaMolpaxbio, Daejeon 34047, KoreaGraduate School of Smart Convergence, Kwangwoon University, Seoul 01897, KoreaDigital pathology analysis using deep learning has been the subject of several studies. As with other medical data, pathological data are not easily obtained. Because deep learning-based image analysis requires large amounts of data, augmentation techniques are used to increase the size of pathological datasets. This study proposes a novel method for synthesizing brain tumor pathology data using a generative model. For image synthesis, we used embedding features extracted from a segmentation module in a general generative model. We also introduce a simple solution for training a segmentation model in an environment in which the masked label of the training dataset is not supplied. As a result of this experiment, the proposed method did not make great progress in quantitative metrics but showed improved results in the confusion rate of more than 70 subjects and the quality of the visual output.https://www.mdpi.com/1424-8220/22/10/3960generative adversarial networkspathology image synthesisdigital pathology |
| spellingShingle | Juwon Kweon Jisang Yoo Seungjong Kim Jaesik Won Soonchul Kwon A Novel Method Based on GAN Using a Segmentation Module for Oligodendroglioma Pathological Image Generation Sensors generative adversarial networks pathology image synthesis digital pathology |
| title | A Novel Method Based on GAN Using a Segmentation Module for Oligodendroglioma Pathological Image Generation |
| title_full | A Novel Method Based on GAN Using a Segmentation Module for Oligodendroglioma Pathological Image Generation |
| title_fullStr | A Novel Method Based on GAN Using a Segmentation Module for Oligodendroglioma Pathological Image Generation |
| title_full_unstemmed | A Novel Method Based on GAN Using a Segmentation Module for Oligodendroglioma Pathological Image Generation |
| title_short | A Novel Method Based on GAN Using a Segmentation Module for Oligodendroglioma Pathological Image Generation |
| title_sort | novel method based on gan using a segmentation module for oligodendroglioma pathological image generation |
| topic | generative adversarial networks pathology image synthesis digital pathology |
| url | https://www.mdpi.com/1424-8220/22/10/3960 |
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