Medical image segmentation using diffusion models
The dissertation presents an advanced exploration of diffusion models for medi cal image segmentation, incorporating prior knowledge to enhance accuracy and reliability. By integrating anatomical and spatial priors, the model achieves sig nificant improvements in segmenting complex structures within...
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| Format: | Thesis-Master by Coursework |
| Language: | English |
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Nanyang Technological University
2024
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| Online Access: | https://hdl.handle.net/10356/175426 |
| _version_ | 1826127366317932544 |
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| author | Chen, Shang |
| author2 | Jiang Xudong |
| author_facet | Jiang Xudong Chen, Shang |
| author_sort | Chen, Shang |
| collection | NTU |
| description | The dissertation presents an advanced exploration of diffusion models for medi cal image segmentation, incorporating prior knowledge to enhance accuracy and reliability. By integrating anatomical and spatial priors, the model achieves sig nificant improvements in segmenting complex structures within the MPI dataset. This research not only showcases the potential of diffusion models in processing medical images but also highlights the benefits of incorporating domain-specific knowledge into machine learning frameworks. The findings suggest a promising direction for future studies, aiming to further refine diagnostic tools and patient care strategies through innovative computational techniques. |
| first_indexed | 2024-10-01T07:07:21Z |
| format | Thesis-Master by Coursework |
| id | ntu-10356/175426 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T07:07:21Z |
| publishDate | 2024 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1754262024-04-26T16:00:26Z Medical image segmentation using diffusion models Chen, Shang Jiang Xudong School of Electrical and Electronic Engineering EXDJiang@ntu.edu.sg Engineering The dissertation presents an advanced exploration of diffusion models for medi cal image segmentation, incorporating prior knowledge to enhance accuracy and reliability. By integrating anatomical and spatial priors, the model achieves sig nificant improvements in segmenting complex structures within the MPI dataset. This research not only showcases the potential of diffusion models in processing medical images but also highlights the benefits of incorporating domain-specific knowledge into machine learning frameworks. The findings suggest a promising direction for future studies, aiming to further refine diagnostic tools and patient care strategies through innovative computational techniques. Master's degree 2024-04-23T04:41:34Z 2024-04-23T04:41:34Z 2024 Thesis-Master by Coursework Chen, S. (2024). Medical image segmentation using diffusion models. Master's thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/175426 https://hdl.handle.net/10356/175426 en application/pdf Nanyang Technological University |
| spellingShingle | Engineering Chen, Shang Medical image segmentation using diffusion models |
| title | Medical image segmentation using diffusion models |
| title_full | Medical image segmentation using diffusion models |
| title_fullStr | Medical image segmentation using diffusion models |
| title_full_unstemmed | Medical image segmentation using diffusion models |
| title_short | Medical image segmentation using diffusion models |
| title_sort | medical image segmentation using diffusion models |
| topic | Engineering |
| url | https://hdl.handle.net/10356/175426 |
| work_keys_str_mv | AT chenshang medicalimagesegmentationusingdiffusionmodels |