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Wang S, Khan A, Lin Y, Jiang Z, Tang H, Alomar SY, Sanaullah M, Bhatti UA. Deep reinforcement learning enables adaptive-image augmentation for automated optical inspection of plant rust. Front Plant Sci 2023; 14:1142957. [PMID: 37484461 PMCID: PMC10360175 DOI: 10.3389/fpls.2023.1142957] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/12/2023] [Accepted: 05/29/2023] [Indexed: 07/25/2023]
Abstract
This study proposes an adaptive image augmentation scheme using deep reinforcement learning (DRL) to improve the performance of a deep learning-based automated optical inspection system. The study addresses the challenge of inconsistency in the performance of single image augmentation methods. It introduces a DRL algorithm, DQN, to select the most suitable augmentation method for each image. The proposed approach extracts geometric and pixel indicators to form states, and uses DeepLab-v3+ model to verify the augmented images and generate rewards. Image augmentation methods are treated as actions, and the DQN algorithm selects the best methods based on the images and segmentation model. The study demonstrates that the proposed framework outperforms any single image augmentation method and achieves better segmentation performance than other semantic segmentation models. The framework has practical implications for developing more accurate and robust automated optical inspection systems, critical for ensuring product quality in various industries. Future research can explore the generalizability and scalability of the proposed framework to other domains and applications. The code for this application is uploaded at https://github.com/lynnkobe/Adaptive-Image-Augmentation.git.
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Affiliation(s)
- Shiyong Wang
- School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou, China
| | - Asad Khan
- Metaverse Research Institute, School of Computer Science and Cyber Engineering, Guangzhou University, Guangzhou, China
| | - Ying Lin
- School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou, China
| | - Zhuo Jiang
- College of Food Science, South China Agricultural University, Guangzhou, China
| | - Hao Tang
- School of Information and Communication Engineering, Hainan University, Haikou, China
| | | | - Muhammad Sanaullah
- Department of Computer Science, Bahauddin Zakariya University, Multan, Pakistan
| | - Uzair Aslam Bhatti
- School of Information and Communication Engineering, Hainan University, Haikou, China
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Li HJ, Liu LZ, Huang Y, Jin YB, Chen XP, Luo W, Su JC, Chen K, Zhang J, Zhang GY. Establishment and Validation of a Novel MRI Radiomics Feature-Based Prognostic Model to Predict Distant Metastasis in Endemic Nasopharyngeal Carcinoma. Front Oncol 2022; 12:794975. [PMID: 35402262 PMCID: PMC8983880 DOI: 10.3389/fonc.2022.794975] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2021] [Accepted: 02/23/2022] [Indexed: 01/12/2023] Open
Abstract
PurposeWe aimed to establish a prognostic model based on magnetic resonance imaging (MRI) radiomics features for individual distant metastasis risk prediction in patients with nasopharyngeal carcinoma (NPC).MethodsRegression analysis was applied to select radiomics features from T1-weighted (T1-w), contrast-enhanced T1-weighted (T1C-w), and T2-weighted (T2-w) MRI scans. All prognostic models were established using a primary cohort of 518 patients with NPC. The prognostic ability of the radiomics, clinical (based on clinical factors), and merged prognostic models (integrating clinical factors with radiomics) were identified using a concordance index (C-index). Models were tested using a validation cohort of 260 NPC patients. Distant metastasis-free survival (DMFS) were calculated by using the Kaplan-Meier method and compared by using the log-rank test.ResultsIn the primary cohort, seven radiomics prognostic models showed similar discrimination ability for DMFS to the clinical prognostic model (P=0.070-0.708), while seven merged prognostic models displayed better discrimination ability than the clinical prognostic model or corresponding radiomics prognostic models (all P<0.001). In the validation cohort, the C-indices of seven radiomics prognostic models (0.645-0.722) for DMFS prediction were higher than in the clinical prognostic model (0.552) (P=0.016 or <0.001) or in corresponding merged prognostic models (0.605-0.678) (P=0.297 to 0.857), with T1+T1C prognostic model (based on Radscore combinations of T1 and T1C Radiomics models) showing the highest C-index (0.722). In the decision curve analysis of the validation cohort for all prognostic models, the T1+T1C prognostic model displayed the best performance.ConclusionsRadiomics models, especially the T1+T1C prognostic model, provided better prognostic ability for DMFS in patients with NPC.
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Affiliation(s)
- Hao-Jiang Li
- Department of Radiology, State Key Laboratory of Oncology in Southern China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-Sen University Cancer Center, Guangzhou, China
| | - Li-Zhi Liu
- Department of Radiology, State Key Laboratory of Oncology in Southern China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-Sen University Cancer Center, Guangzhou, China
| | - Ying Huang
- Department of Radiation Oncology, State Key Laboratory of Oncology in Southern China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-Sen University Cancer Center, Guangzhou, China
| | - Ya-Bin Jin
- Clinical Research Institute, Foshan Academy of Medical Sciences, Sun Yat-Sen University Foshan Hospital and The First People’s Hospital of Foshan, Foshan, China
| | - Xiang-Ping Chen
- Clinical Research Institute, Foshan Academy of Medical Sciences, Sun Yat-Sen University Foshan Hospital and The First People’s Hospital of Foshan, Foshan, China
| | - Wei Luo
- Clinical Research Institute, Foshan Academy of Medical Sciences, Sun Yat-Sen University Foshan Hospital and The First People’s Hospital of Foshan, Foshan, China
| | - Jian-Chun Su
- Department of Radiation Oncology, Foshan Academy of Medical Sciences, Sun Yat-Sen University Foshan Hospital and The First People’s Hospital of Foshan, Foshan, China
| | - Kai Chen
- Department of Radiation Oncology, Foshan Academy of Medical Sciences, Sun Yat-Sen University Foshan Hospital and The First People’s Hospital of Foshan, Foshan, China
| | - Jing Zhang
- Department of Radiation Oncology, Foshan Academy of Medical Sciences, Sun Yat-Sen University Foshan Hospital and The First People’s Hospital of Foshan, Foshan, China
| | - Guo-Yi Zhang
- Department of Radiation Oncology, Foshan Academy of Medical Sciences, Sun Yat-Sen University Foshan Hospital and The First People’s Hospital of Foshan, Foshan, China
- *Correspondence: Guo-Yi Zhang,
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