MSc Thesis: End-to-end prediction on CT images of PDAC patients

Pancreatic ductal adenocarcinoma (PDAC) is considered the fourteenth most common cancer globally, the seventh on the list of causes of cancer death in 2020, and the fourth in 2021. To further investigate the causes and therapy for affected patients we aim to extend our previous work. This project is ideal for students who like to work on applied deep learning. Tasks vary from simple hyperparameter optimisation to dedicated auxillary networks and multi-task learning. If you are interested kindly send us a message including your CV, transcript of records, and very briefly what you are looking for, expecting in your thesis, your talents and interests in regard to this thesis are and if applicable a short description of a project that you did. We are looking forward to your message.


Your qualifications:

  • Enthusiasm for deep learning.
  • Advanced knowledge of machine learning and computer vision.
  • Excellent programming skills in Python and PyTorch.
  • Prior knowledge on survival analysis and statistics is helpful.

What we offer:

  • Exciting research projects with many possibilities to bring in your own ideas.
  • Working in a team of experts in image processing, deep learning, biomedical engineering and medicine.
  • Enough freedom to pursue your own project while providing the guidance you need

References

[1] Kaissis, Georgios, et al. “A machine learning algorithm predicts molecular subtypes in pancreatic ductal adenocarcinoma with differential response to gemcitabine-based versus FOLFIRINOX chemotherapy.” PloS one 14.10 (2019): e0218642.
[2] Harder, Felix N., et al. “[18F] FDG PET/MRI enables early chemotherapy response prediction in pancreatic ductal adenocarcinoma.” EJNMMI research 11.1 (2021): 1-11.

Alexander Ziller
Alexander Ziller
PhD Student

My research interests include deep learning in medical imaging as well as Privacy-preserving Machine Learning.