University of Groningen
Deep learning for lung cancer on computed tomography
Zheng, Sunyi
DOI:
10.33612/diss.171374829
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Publication date:
2021
Link to publication in University of Groningen/UMCG research database
Citation for published version (APA):
Zheng, S. (2021). Deep learning for lung cancer on computed tomography: early detection and prognostic
prediction. University of Groningen. https://doi.org/10.33612/diss.171374829
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STELLINGEN
horend bij het proefschrift
Deep Learning for Lung Cancer on Computed Tomography
Early detection and prognostic prediction
1. By mimicking the clinical procedure of doctors, the performance of deep learning can be improved in lung nodule detection. (Chapter 2)
2. Using a deep learning-based nodule detection system in a lung cancer screening setting, most negative scans can be safely excluded from the reading list of radiologists. (Chapter 3)
3. Multi-stage deep learning systems outperform the systems that consist of only one stage. (Chapter 4)
4. When findings on three orthogonal planes are combined, deep learning can recognize all the nodules regardless of type and size if training data is sufficient. (Chapter 5)
5. A deep learning algorithm that integrates clinical variables and image features extracted from CT scans can stratify patients into different mortality risk groups. (Chapter 6)
6. Artificial intelligence systems will partly automate the diagnostic process of lung cancer screening and support the majority of the clinical work performed by radiologists.
7. In the near future, deep learning systems can train junior radiologists in pulmonary image analysis.
8. A PhD student is a supervised learning system at the beginning and gradually becomes a semi-supervised learning system.
9. Man approaches the unattainable truth through a succession of errors. - Aldous Huxley
10. Tell me and I forget; Show me and I remember. Involve me and I understand. - Xunzi 不闻不若闻之，闻之不若见之；见之不若知之，知之不若行之；学至于 行而止矣。
Sunyi Zheng
