Dissertation/ Thesis
Integration and processing of large-scale biomedical data
| Τίτλος: | Integration and processing of large-scale biomedical data |
|---|---|
| Συγγραφείς: | Zhang, Wenhua, 张闻华 |
| Συνεισφορές: | Pan, J, Wang, WP |
| Στοιχεία εκδότη: | The University of Hong Kong (Pokfulam, Hong Kong) |
| Έτος έκδοσης: | 2023 |
| Συλλογή: | University of Hong Kong: HKU Scholars Hub |
| Θεματικοί όροι: | Medical informatics - Data processing, Biomedical engineering - Data processing |
| Περιγραφή: | With the improvements in data collection methods, high-quality data abounds in medical imaging and other fields. While many algorithms have emerged to detect, segment, or classify the data, few have proposed methods to re-organize or mine them. It is important to develop approaches to dive into the data and fully exploit them. This thesis tackles three dataset integration and processing problems of large-scale biomedical data: labeled dataset merging, unlabeled dataset self-supervised training, and scalable volumetric data mesh generating. The first part of this thesis addresses the problem of integrating inconsistent datasets. A large number of labeled data is required to train effective nucleus classification models. However, it is challenging to label a large-scale nucleus classification dataset, considering that high-quality labeling requires specific domain knowledge and tremendous efforts. In addition, existing public datasets are often inconsistently labeled. Due to this inconsistency, conventional models tend to work independently to infer their classification results, thus limiting the classification performance. To fully utilize all annotated datasets, we propose a method to integrate all the available annotated datasets. Specifically, we formulate the problem as a multi-label problem with missing labels. Thus, we can utilize all the datasets in a unified framework. Besides the substantial improvement compared to other methods, our result dataset also has a uniform format which can help future research on nucleus classification. The second part of this thesis addresses the problem of representation learning for nucleus instance classification. Unlike the limited scale of annotated data, unlabeled data is usually of large scale. Thus, we aim to design a self-supervised method for representation learning on unlabeled datasets to alleviate the burden of data annotation. Moreover, previous methods often downplay the contextual information that is critical for classification. To explicitly provide the ... |
| Τύπος εγγράφου: | doctoral or postdoctoral thesis |
| Γλώσσα: | English |
| Relation: | HKU Theses Online (HKUTO); 991044705906303414; https://hub.hku.hk/handle/10722/328917 |
| Διαθεσιμότητα: | https://hub.hku.hk/handle/10722/328917 |
| 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.3FAC5B27 |
| Βάση Δεδομένων: | BASE |
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