DC Field | Value | Language |
---|---|---|
dc.contributor.advisor | 예종철 | - |
dc.contributor.author | Huh, Jaeyoung | - |
dc.contributor.author | 허재영 | - |
dc.date.accessioned | 2024-08-08T19:31:06Z | - |
dc.date.available | 2024-08-08T19:31:06Z | - |
dc.date.issued | 2024 | - |
dc.identifier.uri | http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=1099209&flag=dissertation | en_US |
dc.identifier.uri | http://hdl.handle.net/10203/322015 | - |
dc.description | 학위논문(박사) - 한국과학기술원 : 바이오및뇌공학과, 2024.2,[x, 96 p. :] | - |
dc.description.abstract | Recently, deep learning techniques have advanced significantly contributing to notable progress in the medical field. In particular, the unsupervised method is valuable in the medical field where acquiring labeled datasets can be challenging. Another notable approach involves the pre-trained model, which was trained on a large-scale dataset encompassing both vision and text data. This model achieved contextual and cross-modal understanding, showing significant performance improvements in various tasks. this study introduces a method aimed at addressing various challenges related to image quality degradation in the medical imaging field and enhancing workflow. I introduced a method for translating 3-dimensional (3-D) ultrasound image quality to 2-D quality, enhancing contrast, sharpness, and reducing artifacts. Additionally, I proposed a method for RealisticVue image restoration and enhancement network, aiming to generate a realistic full facial view of the fetus for a more pleasing experience for pregnant. Furthermore, I suggested a method for ultrasound image translation using speech guidance to achieve image control without direct handling. Moreover, I presented a method for text correction in the medical domain, generating from conventional Speech-To-Text (STT) systems by leveraging vision information. Finally, I proposed a method for breast ultrasound report generation using an LLM-based LangChain framework, addressing the time-consuming issues. I demonstrate that my approach holds considerable promise for practical implementation in the clinical field. | - |
dc.language | eng | - |
dc.publisher | 한국과학기술원 | - |
dc.subject | 딥러닝▼a비지도학습▼a사전 학습 모델▼a의료 영상▼a화질 개선▼a작업 흐름 개선 | - |
dc.subject | Deep learning▼aUnsupervised learning▼aPre-trained model▼aMedical imaging▼aImage quality improvement▼aWorkflow improvement | - |
dc.title | Unsupervised, pre-trained models for image quality and workflow improvement in medical application | - |
dc.title.alternative | 비지도 학습, 사전 학습 모델 기반 의료 어플리케이션에서의 이미지 화질 및 작업흐름 개선 | - |
dc.type | Thesis(Ph.D) | - |
dc.identifier.CNRN | 325007 | - |
dc.description.department | 한국과학기술원 :바이오및뇌공학과, | - |
dc.contributor.alternativeauthor | Ye, Jong Chul | - |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.