Deep Semi-Supervised Point Cloud-Based Models with Uncertainty Awareness for Efficient Abnormality Detection in 3D Medical Imaging
Chen, Yubo (2025) Deep Semi-Supervised Point Cloud-Based Models with Uncertainty Awareness for Efficient Abnormality Detection in 3D Medical Imaging. Master of Philosophy thesis, Queensland University of Technology.
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Description
This thesis develops an efficient AI method to spot abnormalities in 3-D medical scans when labelled data are scarce. We convert organ shapes from CT images into simple “point cloud” models and analyse them with a specialized 3-D network trained using a new semi-supervised, uncertainty-aware strategy. Using an adrenal-gland dataset, our approach improves detection of abnormal cases while reporting how confident the AI is, helping clinicians judge when to trust or further review results. The framework reduces computing cost, handles imbalanced data, and offers a pathway toward safer, scalable decision support in medical imaging.
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| ID Code: | 259810 |
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| Item Type: | QUT Thesis (Master of Philosophy) |
| Supervisor: | Gu, YuanTong, Alzubaidi, Laith, & Gammulle, Pranali Harshala |
| Keywords: | Medical Abnormal Automatic Detection, Deep Learning, Point Cloud Classification, PointNet, PointNet++, Uncertainty Estimation, Supervised Learning, Semi-Supervised Learning, Mont Carlo Dropout, Medical Three-Dimensional (3D) Data |
| DOI: | 10.5204/thesis.eprints.259810 |
| Divisions: | Current > QUT Faculties and Divisions > Faculty of Engineering Current > Schools > School of Mechanical, Medical & Process Engineering |
| Institution: | Queensland University of Technology |
| Deposited On: | 05 Sep 2025 09:35 |
| Last Modified: | 08 Sep 2025 07:55 |
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