AI for Health
Thema: AI for Health
DozentIn(en): Prof. Roland Eils, Julius Upmeier zu Belzen, Benjamin Wild, Sören Lukassen
Maximale Teilnehmerzahl: 10
Zeitraum/Vorbesprechungstermin: nach Absprache
In this software project, you will learn about the fundamentals of applying machine learning to problems in the healthcare domain. We will provide access to public datasets and an environment for development (including ECG, Microscopy, and X-Ray datasets). Furthermore, we are open to discuss specific interests or ideas you might have in our initial meeting, and define the project accordingly. In any case, we will develop a pipeline from dataset access, preprocessing, baseline models, ML models and evaluation, as well as planning of future applications and extensions.
We are flexible with regards to the project timeline. If you have any further questions, please feel free to email us:
benjamin.wild@bih-charite.de, julius.upmeier@bih-charite.de, soeren.lukassen@bih-charite.de
Ort: Digital (A), BIH, Kapelle-Ufer 2 (B)
Kurze inhaltliche Beschreibung:
Project: Machine Learning in Medicine: from idea to tool
- Learn the fundamentals of developing, training and testing deep learning models in the medical domain
- Learn about relevant metrics for evaluation and benchmarking and potential biases to watch out for
- Optional: Work on integrating the developed and evaluated models into usable (web) tool
Quantitative Aufteilung: (in %)
Praktische Programmierarbeit: 75%
Soft Skills: 25%
Verwendete Programmiersprache(n): Python (>90%), maybe some javascript for web app
Schwierigkeitsgrad (Acht Sterne verteilt auf drei Bereiche):
A Programmieren ****
B Biologie/Chemie *
C Projektmanagement ***
Erforderliche Vorkenntnisse:
- Experience with the Python programming language
- Fundamental understanding of “What is machine learning"
- Preferably prior experience with PyTorch or other DL-Libraries
- Understanding of neural networks and preferably experience with deep learning
Kontaktadresse, Webseite/Link:
