Dissertation/ Thesis
Deep learning based image analysis with enhanced reasoning capability
| Τίτλος: | Deep learning based image analysis with enhanced reasoning capability |
|---|---|
| Συγγραφείς: | Zhao, Gangming, 趙剛明 |
| Συνεισφορές: | Yu, Y |
| Στοιχεία εκδότη: | The University of Hong Kong (Pokfulam, Hong Kong) |
| Έτος έκδοσης: | 2024 |
| Συλλογή: | University of Hong Kong: HKU Scholars Hub |
| Θεματικοί όροι: | Image analysis - Data processing, Deep learning (Machine learning) |
| Περιγραφή: | To create high-quality hybrid models interactively based on Convolutional Neural Networks (CNNs) or by conducting cross-module learning from diverse feature representations usually suffers from the challenging task of combining multiple latency information from a sparse input, such as a semantic object, a medical image, and images with special topology. In this thesis, we use deep learning strategies to present novel algorithms for three problems: representing the basic object semantic information, creating a hybrid CNNs and Graph Neural Networks (GNNs) model for 3D nodule recognition, and learning special topological information for 3D medical vessel images. At first, to represent the object semantic information, we proposed a novel Graph Feature Pyramid Network (GraphFPN). Feature pyramids have been proven powerful in image understanding tasks that require multi-scale features. State-of-the-art methods for multi-scale feature learning focus on performing feature interactions across space and scales using neural networks with a fixed topology. In this section, we propose graph feature pyramid networks capable of adapting their topological structures to varying intrinsic image structures and supporting simultaneous feature interactions across all scales. We first define an image-specific superpixel hierarchy for each input image to represent its intrinsic image structures. The graph feature pyramid network inherits its structure from this superpixel hierarchy. Contextual and hierarchical layers are designed to achieve feature interactions within the same scale and across different scales, respectively. In clinical practice, doctors often use attributes, e.g. morphological and appearance characteristics of a lesion, to aid disease diagnosis. Effectively modeling all relationships among attributes could boost the accuracy of medical image diagnosis. In this section, we introduce a hybrid neural-probabilistic reasoning algorithm for interpretable attribute-based medical image diagnosis. There are two parallel ... |
| Τύπος εγγράφου: | doctoral or postdoctoral thesis |
| Γλώσσα: | English |
| Relation: | HKU Theses Online (HKUTO); Zhao, G. [趙剛明]. (2024). Deep learning based image analysis with enhanced reasoning capability. (Thesis). University of Hong Kong, Pokfulam, Hong Kong SAR.; 991044809206303414; https://hub.hku.hk/handle/10722/343770 |
| Διαθεσιμότητα: | https://hub.hku.hk/handle/10722/343770 |
| Rights: | The author retains all proprietary rights, (such as patent rights) and the right to use in future works. ; This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. |
| Αριθμός Καταχώρησης: | edsbas.A54D36A9 |
| Βάση Δεδομένων: | BASE |
καταχωρήστε σχόλιο πρώτοι!