| Author: | Zhu, Jiarui |
| Title: | A clinically interpretable feature extraction approach based on 3D-gaussian for medical imaging analysis |
| Advisors: | Cai, Jing (HTI) Ren, Ge (HTI) |
| Degree: | Ph.D. |
| Year: | 2026 |
| Department: | Department of Health Technology and Informatics |
| Pages: | xv, 124 pages : color illustrations |
| Language: | English |
| Abstract: | Although advanced deep learning models especially foundation models have dominated in most area of medical imaging analysis with remarkable performance, the implicitness of the feature representing and downstream generating process potentially bottlenecks their performance and draws serious clinical concerns on their interpretability during clinical deployment. In order to break the performance barrier and achieve cogent interpretability demonstration, in this study we develop a 3D gaussian representing framework with explicit knowledge-content-analyzable representations and knowledge-source-traceable generating process for medical imaging. Specifically, tailored frameworks are developed based on a newly emerging 3D scene representing technique for addressing long-standing challenges in the area of medical imaging segmentation, image-to-image translation and multimodal fusion. By leveraging the unique advantages that 3D gaussian representations possess, including highly-refined 3D representation, editable feature space, cross-domain adaptive distribution and a traceable generating process, these frameworks are able to address main challenges including incomplete or incorrect information extraction, suboptimal optimization target, ineffective training guidance and unreliable generated results. Exclusive experiments are conducted to evaluate the clinical efficacy of the proposed method with comparison to state-of-the-art methods, and the quantitative, qualitative and ablation analysis have demonstrated the superiority of the proposed method. This study provides a novel and robust interpretable paradigm for medical imaging analysis which is profoundly meaningful for a wide range of clinical applications. |
| Rights: | All rights reserved |
| Access: | open access |
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