Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.advisor | 박상현 | - |
| dc.contributor.author | Euijin Jung | - |
| dc.date.accessioned | 2023-09-18T21:00:40Z | - |
| dc.date.available | 2023-09-18T21:00:40Z | - |
| dc.date.issued | 2023 | - |
| dc.identifier.uri | http://hdl.handle.net/20.500.11750/46394 | - |
| dc.identifier.uri | http://dgist.dcollection.net/common/orgView/200000686643 | - |
| dc.description | Conditional Image Generation; Deep Convolutional Neural Networks; Generative Adversarial Networks; Diffusion Models; MRI; Alzheimer’s disease | - |
| dc.description.tableofcontents | I. INTRODUCTION 1 1 Background and Motivation 1 2 Main Contributions 1 3 Thesis Outline 1 II. Paired Image-to-Image Translation for Perivascular Spaces Enhancement 3 1 Introduction 3 1.1 Related Works 4 1.2 Contributions 5 2 Methodology 6 2.1 Densely Connected Deep Neural Network 6 2.2 Implementation Details 8 3 Experiments and Results 8 3.1 Data set 8 3.2 Evaluation Settings 11 3.3 Quantitative Results 11 3.4 Qualitative Results 12 3.5 Discussion for comparison networks 12 3.6 Discussion for network depth 13 4 Discussion 14 III. Multi-domain Image-to-Image Translation for Alzheimer's disease progression 18 1 Introduction 18 1.1 Related Works 20 2 Methodology 22 2.1 Objective function 24 3 Experimental settings 26 4 Results 28 4.1 Quantitative results 28 4.2 Qualitative results 31 4.3 Comparison of Subcortical Structures 33 4.4 Ablation study 33 4.5 Computational efficiency 35 5 Discussion 36 IV. Paired Image-to-Image Translation using Guided Diffusion Model 37 1 Introduction 37 2 Methodology 38 2.1 Training Stage 41 2.2 Inference Stage 41 3 Experiments 42 3.1 Implementation details 42 3.2 Quantitative Results 44 3.3 Qualitative Results 45 4 Discussion 45 V. Multi-domain Image-to-Image Translation using Guided Diffusion Model 51 1 Introduction 51 2 Methodology 51 2.1 Training Stage 53 2.2 Inference Stage 53 3 Experiments 53 3.1 Implementation details 53 3.2 Quantitative Results 55 3.3 Qualitative Results 55 4 Discussion 56 VI. Conclusion and Future Directions 58 1 Conclusion 58 2 Future Directions 58 VII. Acknowledgement 60 References 62 |
- |
| dc.format.extent | 70 | - |
| dc.language | eng | - |
| dc.publisher | DGIST | - |
| dc.title | Condition and Modality-guided Medical Image Generation using Deep Learning-based Generative Models | - |
| dc.title.alternative | 딥러닝 기반 생성 모델을 사용한 조건부 및 모달리티 가이드 의료영상 생성 기법 | - |
| dc.type | Thesis | - |
| dc.identifier.doi | 10.22677/THESIS.200000686643 | - |
| dc.description.degree | Doctor | - |
| dc.contributor.department | Department of Robotics and Mechatronics Engineering | - |
| dc.contributor.coadvisor | Kyong Hwan Jin | - |
| dc.date.awarded | 2023-08-01 | - |
| dc.publisher.location | Daegu | - |
| dc.description.database | dCollection | - |
| dc.citation | XT.RD 정67 202308 | - |
| dc.date.accepted | 2023-09-14 | - |
| dc.contributor.alternativeDepartment | 로봇및기계전자공학과 | - |
| dc.subject.keyword | Conditional Image Generation | - |
| dc.subject.keyword | Deep Convolutional Neural Networks | - |
| dc.subject.keyword | Generative Adversarial Networks | - |
| dc.subject.keyword | Diffusion Models | - |
| dc.subject.keyword | MRI | - |
| dc.subject.keyword | Alzheimer’s disease | - |
| dc.contributor.affiliatedAuthor | Euijin Jung | - |
| dc.contributor.affiliatedAuthor | Sang Hyun Park | - |
| dc.contributor.affiliatedAuthor | Kyong Hwan Jin | - |
| dc.contributor.alternativeName | 정의진 | - |
| dc.contributor.alternativeName | Sang Hyun Park | - |
| dc.contributor.alternativeName | 진경환 | - |
| dc.rights.embargoReleaseDate | 2028-08-31 | - |
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