Retinal disease projection conditioning by biological traits
Abstract Fundus image captures rear of an eye which has been studied for disease identification, classification, segmentation, generation, and biological traits association using handcrafted, conventional, and deep learning methods. In biological traits estimation, most of the studies have been carr...
| Main Authors: | , , , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Springer
2023-07-01
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| Series: | Complex & Intelligent Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s40747-023-01141-0 |
| _version_ | 1797272204144017408 |
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| author | Muhammad Hassan Hao Zhang Ahmed Ameen Fateh Shuyue Ma Wen Liang Dingqi Shang Jiaming Deng Ziheng Zhang Tsz Kwan Lam Ming Xu Qiming Huang Dongmei Yu Canyang Zhang Zhou You Wei Pang Chengming Yang Peiwu Qin |
| author_facet | Muhammad Hassan Hao Zhang Ahmed Ameen Fateh Shuyue Ma Wen Liang Dingqi Shang Jiaming Deng Ziheng Zhang Tsz Kwan Lam Ming Xu Qiming Huang Dongmei Yu Canyang Zhang Zhou You Wei Pang Chengming Yang Peiwu Qin |
| author_sort | Muhammad Hassan |
| collection | DOAJ |
| description | Abstract Fundus image captures rear of an eye which has been studied for disease identification, classification, segmentation, generation, and biological traits association using handcrafted, conventional, and deep learning methods. In biological traits estimation, most of the studies have been carried out for the age prediction and gender classification with convincing results. The current study utilizes the cutting-edge deep learning (DL) algorithms to estimate biological traits in terms of age and gender together with associating traits to retinal visuals. For the trait’s association, we embed aging as the label information into the proposed DL model to learn knowledge about the effected regions with aging. Our proposed DL models named FAG-Net and FGC-Net, which correspondingly estimates biological traits (age and gender) and generates fundus images. FAG-Net can generate multiple variants of an input fundus image given a list of ages as conditions. In this study, we analyzed fundus images and their corresponding association in terms of aging and gender. Our proposed models outperform randomly selected state-of-the-art DL models. |
| first_indexed | 2024-03-07T14:25:00Z |
| format | Article |
| id | doaj.art-2d5daa167c4c4997b21818b7bbde196d |
| institution | Directory Open Access Journal |
| issn | 2199-4536 2198-6053 |
| language | English |
| last_indexed | 2024-03-07T14:25:00Z |
| publishDate | 2023-07-01 |
| publisher | Springer |
| record_format | Article |
| series | Complex & Intelligent Systems |
| spelling | doaj.art-2d5daa167c4c4997b21818b7bbde196d2024-03-06T08:07:36ZengSpringerComplex & Intelligent Systems2199-45362198-60532023-07-0110125727110.1007/s40747-023-01141-0Retinal disease projection conditioning by biological traitsMuhammad Hassan0Hao Zhang1Ahmed Ameen Fateh2Shuyue Ma3Wen Liang4Dingqi Shang5Jiaming Deng6Ziheng Zhang7Tsz Kwan Lam8Ming Xu9Qiming Huang10Dongmei Yu11Canyang Zhang12Zhou You13Wei Pang14Chengming Yang15Peiwu Qin16Center of Precision Medicine and Healthcare, Tsinghua-Berkeley Shenzhen InstituteCenter of Precision Medicine and Healthcare, Tsinghua-Berkeley Shenzhen InstituteDepartment of Radiology, Shenzhen Children’s HospitalCenter of Precision Medicine and Healthcare, Tsinghua-Berkeley Shenzhen InstituteCenter of Precision Medicine and Healthcare, Tsinghua-Berkeley Shenzhen InstituteCenter of Precision Medicine and Healthcare, Tsinghua-Berkeley Shenzhen InstituteCenter of Precision Medicine and Healthcare, Tsinghua-Berkeley Shenzhen InstituteCenter of Precision Medicine and Healthcare, Tsinghua-Berkeley Shenzhen InstituteCenter of Precision Medicine and Healthcare, Tsinghua-Berkeley Shenzhen InstituteDepartment of Automation, Tsinghua UniversityShenzhen ZNV Technology Co., LtdSchool of Mechanical, Electrical & Information Engineering, Shandong UniversityCenter of Precision Medicine and Healthcare, Tsinghua-Berkeley Shenzhen InstituteCollege of Computer Science and Technology, Jilin UniversitySchool of Mathematical and Computer Sciences, Heriot-Watt UniversityUniversity of Science and Technology HospitalCenter of Precision Medicine and Healthcare, Tsinghua-Berkeley Shenzhen InstituteAbstract Fundus image captures rear of an eye which has been studied for disease identification, classification, segmentation, generation, and biological traits association using handcrafted, conventional, and deep learning methods. In biological traits estimation, most of the studies have been carried out for the age prediction and gender classification with convincing results. The current study utilizes the cutting-edge deep learning (DL) algorithms to estimate biological traits in terms of age and gender together with associating traits to retinal visuals. For the trait’s association, we embed aging as the label information into the proposed DL model to learn knowledge about the effected regions with aging. Our proposed DL models named FAG-Net and FGC-Net, which correspondingly estimates biological traits (age and gender) and generates fundus images. FAG-Net can generate multiple variants of an input fundus image given a list of ages as conditions. In this study, we analyzed fundus images and their corresponding association in terms of aging and gender. Our proposed models outperform randomly selected state-of-the-art DL models.https://doi.org/10.1007/s40747-023-01141-0Fundus imagesBiological traitsAgeGenderGANAging effects |
| spellingShingle | Muhammad Hassan Hao Zhang Ahmed Ameen Fateh Shuyue Ma Wen Liang Dingqi Shang Jiaming Deng Ziheng Zhang Tsz Kwan Lam Ming Xu Qiming Huang Dongmei Yu Canyang Zhang Zhou You Wei Pang Chengming Yang Peiwu Qin Retinal disease projection conditioning by biological traits Complex & Intelligent Systems Fundus images Biological traits Age Gender GAN Aging effects |
| title | Retinal disease projection conditioning by biological traits |
| title_full | Retinal disease projection conditioning by biological traits |
| title_fullStr | Retinal disease projection conditioning by biological traits |
| title_full_unstemmed | Retinal disease projection conditioning by biological traits |
| title_short | Retinal disease projection conditioning by biological traits |
| title_sort | retinal disease projection conditioning by biological traits |
| topic | Fundus images Biological traits Age Gender GAN Aging effects |
| url | https://doi.org/10.1007/s40747-023-01141-0 |
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