Artificial intelligence in medical physics

Regular enrollment.

Specific use of artificial intelligence (AI) in medicine:

• Use of convolutional neural networks (CNN) or similar in quantitative analysis of medical images
• Use of recursive or similar networks in analysis of longitudinal data
• AI in predictive models, comparsion to standard statistical methods

Methods of evaluation of appropriatnes of UI in medical applications:

• Out of domain detection
• Prediction uncertainty
• Quantification of interpretability
• Transfer and supplementary learning using new cases

• LeCun, Y., Bengio, Y. & Hinton, G. Deep learning. Nature 521, 436–444 (2015)

• Gonzalez C, Gotkowski K, Fuchs M, et al. Distance-based detection of out-of-distribution silent failures for Covid-19 lung lesion segmentation. Medical Image Analysis 82 2022: 102596.

• Abdar, M, Pourpanah F, Hussain S, et al. A Review of Uncertainty Quantification in Deep Learning: Techniques, Applications and Challenges. Information Fusion 76 2021: 243-97.

• Samek, W.; Binder, A.; Montavon, G.;Lapuschkin, S.; and M¨uller, K. 2017. Evaluating the visu-alization of what a deep neural network has learned. IEEE Trans. Neural Netw. Learning Syst. 28(11):2660–2673.

• Pan, S.J., & Yang, Q. (2010). A Survey on Transfer Learning. IEEE Transactions on Knowledge and Data Engineering, 22, 1345-1359.

Students become familiar with general aspects of use of AI in medicine, and evaluation of performance for designated tasks in particular.

Course specific competences:

Understanding and identification of common principles of using AI in medicine. Familiarity with most common use cases. Understanding of methods of evaluation of AI for a particular purpuse. Familiarity with most common problems and limitations of AI in medicine.

Knowledge and understanding:

Physics and mathematics background of most common AI methods in medicine. Methods to evaluate fitness of AI to solve specific problems.

Use:

Examples of use of AI in medicine – prediction of risk in breast screening, segmentation of cancer on PET/CT images, identification of dementia based on PET/CT images. Practical evaluation of method uncertainty, out of distribution detection, interpretability quantification and transfer learning.

Reflection:

Understanding and identification of AI limitations. Illustration of performance drop for complex problems and decisions.

Lectures, homeworks, consultations.

Practical homework
5 - 10, a student passes the exam if he is graded from 6 to 10

Andrej Studen:

• Hribernik N, Huff DT, Studen A, et al. Quantitative imaging biomarkers of immune-related adverse events in immune-checkpoint blockade-treated metastatic melanoma patients: a pilot study. Eur J Nucl Med Mol Imaging. 2022;49(6):1857-1869.
• Klanecek Z, Wagner T, Wang YK et al. Uncertainty estimation for deep learning-based pectoral muscle segmentation via Monte Carlo dropout. Phys. Med. Biol. 68 2023: 115007.
• Studen A, Clinthorne N. System resolution versus image uncertainty for positron emission tomography scanners. J Med Imaging (Bellingham). 2022;9(3):033501.