Explainable machine learning methods in the medical domain
| Thesis title in Czech: | Interpretovatelnost metod strojového učení v medicínské doméně |
|---|---|
| Thesis title in English: | Explainable machine learning methods in the medical domain |
| Key words: | strojové učení|interpretovatelnost|medicínské data |
| English key words: | machine learning|explainability|medical data |
| Academic year of topic announcement: | 2021/2022 |
| Thesis type: | dissertation |
| Thesis language: | angličtina |
| Department: | Department of Software and Computer Science Education (32-KSVI) |
| Supervisor: | doc. RNDr. Elena Šikudová, Ph.D. |
| Author: | hidden - assigned and confirmed by the Study Dept. |
| Date of registration: | 08.03.2022 |
| Date of assignment: | 08.03.2022 |
| Confirmed by Study dept. on: | 08.03.2022 |
| Guidelines |
| Concerns regarding potential risks, and trust issues in the medical domain originate in the un-explainability of machine learning methods. This work should focus approaches towards making ML models explainable. |
| References |
| Gonzalez, R. C. & Woods, R. E. (2008), Digital image processing, Prentice Hall, Upper Saddle River, N.J.
Goodfellow et. al. (2016), Deep Learning, MIT Press Samek et. al. (2019), Explainable AI: Interpreting, Explaining and Visualizing Deep Learning, Lecture Notes in Computer Science, Springer, Cham Islam, et. al. (2022), A Systematic Review of Explainable Artificial Intelligence in Terms of Different Application Domains and Tasks. Appl. Sci. 2022, 12, 1353 |