Dissertation/ Thesis
Integration and processing of large-scale biomedical data
| Title: | Integration and processing of large-scale biomedical data |
|---|---|
| Authors: | Zhang, Wenhua, 张闻华 |
| Contributors: | Pan, J, Wang, WP |
| Publisher Information: | The University of Hong Kong (Pokfulam, Hong Kong) |
| Publication Year: | 2023 |
| Collection: | University of Hong Kong: HKU Scholars Hub |
| Subject Terms: | Medical informatics - Data processing, Biomedical engineering - Data processing |
| Description: | 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 ... |
| Document Type: | doctoral or postdoctoral thesis |
| Language: | English |
| Relation: | HKU Theses Online (HKUTO); 991044705906303414; https://hub.hku.hk/handle/10722/328917 |
| Availability: | 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. |
| Accession Number: | edsbas.3FAC5B27 |
| Database: | BASE |
| FullText | Text: Availability: 0 CustomLinks: – Url: https://hub.hku.hk/handle/10722/328917# Name: EDS - BASE (ns324271) Category: fullText Text: View record from BASE |
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| Header | DbId: edsbas DbLabel: BASE An: edsbas.3FAC5B27 RelevancyScore: 851 AccessLevel: 3 PubType: Dissertation/ Thesis PubTypeId: dissertation PreciseRelevancyScore: 851.480346679688 |
| IllustrationInfo | |
| Items | – Name: Title Label: Title Group: Ti Data: Integration and processing of large-scale biomedical data – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Zhang%2C+Wenhua%22">Zhang, Wenhua</searchLink><br /><searchLink fieldCode="AR" term="%22张闻华%22">张闻华</searchLink> – Name: Author Label: Contributors Group: Au Data: Pan, J<br />Wang, WP – Name: Publisher Label: Publisher Information Group: PubInfo Data: The University of Hong Kong (Pokfulam, Hong Kong) – Name: DatePubCY Label: Publication Year Group: Date Data: 2023 – Name: Subset Label: Collection Group: HoldingsInfo Data: University of Hong Kong: HKU Scholars Hub – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Medical+informatics+-+Data+processing%22">Medical informatics - Data processing</searchLink><br /><searchLink fieldCode="DE" term="%22Biomedical+engineering+-+Data+processing%22">Biomedical engineering - Data processing</searchLink> – Name: Abstract Label: Description Group: Ab Data: 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 ... – Name: TypeDocument Label: Document Type Group: TypDoc Data: doctoral or postdoctoral thesis – Name: Language Label: Language Group: Lang Data: English – Name: NoteTitleSource Label: Relation Group: SrcInfo Data: HKU Theses Online (HKUTO); 991044705906303414; https://hub.hku.hk/handle/10722/328917 – Name: URL Label: Availability Group: URL Data: https://hub.hku.hk/handle/10722/328917 – Name: Copyright Label: Rights Group: Cpyrght Data: 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. – Name: AN Label: Accession Number Group: ID Data: edsbas.3FAC5B27 |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsbas&AN=edsbas.3FAC5B27 |
| RecordInfo | BibRecord: BibEntity: Languages: – Text: English Subjects: – SubjectFull: Medical informatics - Data processing Type: general – SubjectFull: Biomedical engineering - Data processing Type: general Titles: – TitleFull: Integration and processing of large-scale biomedical data Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Zhang, Wenhua – PersonEntity: Name: NameFull: 张闻华 – PersonEntity: Name: NameFull: Pan, J – PersonEntity: Name: NameFull: Wang, WP IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Type: published Y: 2023 Identifiers: – Type: issn-locals Value: edsbas – Type: issn-locals Value: edsbas.oa |
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