Mehmet Dalmis Automated analysis of breast MRI: From Traditional Methods into Deep Learning
This thesis shows the strong potential of deep learning algorithms to replace traditional computer algorithms for breast MRI and help radiologists interpret the complex information obtained from these (radiological) images.read more
Mehmet Dalmis Automated analysis of breast MRI: From Traditional Methods into Deep LearningAdvances in radiology improve healthcare by providing more detailed information about various diseases, such as cancer. However, increased complexity and information in radiological images, together with screening programs also increase workload of radiologists. Can computers help by automating some of their tasks? Researchers have been working on the use of computers in radiological image interpretation for a long time with limited success. However, the recently popularized technique ‘deep learning’ is changing this, leading to new artificial intelligence systems that perform at the accuracy level of the radiologists. This thesis focuses on the breast MRI dimension of this transition. It presents deep learning based artificial intelligence systems for automated reading of breast MRI. Results show the strong potential of deep learning algorithms to replace traditional computer algorithms for breast MRI and help radiologists interpret the complex information obtained from these (radiological) images.
Date, time and location PhD defense
- Date: 12 September 2019
- Time: 12.30 hrs
- Location: Radboud Universiteit, Academiezaal Aula, Comeniuslaan 2
Mehmet Ufuk Dalmış (1982) studied Electrical and Electronics Engineering and Biomedical Engineering before starting his PhD at the Radboud university medical center, where he carried out research on artificial intelligence methods for automated reading of breast MRI. Currently, he is working in ScreenPoint Medical to apply similar methods to mammography and tomosynthesis to automate reading in breast screening programs.
- Promotor: Prof. N. Karssemeijer
- Co-promotor: R.M. Mann PhD, A. Gubern Merida PhD and J.J.B. Teuwen PhD