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https://hdl.handle.net/10216/169698| Author(s): | Afonso José Pinheiro Oliveira Esteves Abreu |
| Title: | Overcoming scarce annotations through deep semi-supervised learning in cancer characterisation |
| Issue Date: | 2025-09-19 |
| Description: | The development of computer-aided systems for cancer characterisation has been an intense research field, considering the value of the outcomes for more adequate and personalized treatment plan assignments to increase survival chances. Medical images have been shown to provide valuable information on the biological phenomena associated with cancer development, which can be used to design Artificial Intelligence (AI)-based predictive models for support in the clinical routine. From the clinical side, the imaging annotation process can be extremely difficult, subjective and time-consuming, which motivates the development of models that can benefit from labelled and non-labeled data. |
| Subject: | Engenharia electrotécnica, electrónica e informática Electrical engineering, Electronic engineering, Information engineering |
| Scientific areas: | Ciências da engenharia e tecnologias::Engenharia electrotécnica, electrónica e informática Engineering and technology::Electrical engineering, Electronic engineering, Information engineering |
| TID identifier: | 204109906 |
| URI: | https://hdl.handle.net/10216/169698 |
| Document Type: | Dissertação |
| Rights: | openAccess |
| Appears in Collections: | FEUP - Dissertação |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| 743674.pdf | Overcoming scarce annotations through deep semi-supervised learning in cancer characterisation | 1.99 MB | Adobe PDF |  View/Open |
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