Machine Learning for Medical Image Analysis (Seminar)
|Course title:||Machine Learning for Medical Image Analysis|
|Lecturers:||Dr. Lisa Koch, Dr. Christian Baumgartner|
|Teaching assistants:||Paul Fischer, Indu Ilanchezian, Nikolas Morshuis, Sarah Müller, Stefano Woerner|
|Location:||Maria-von-Linden-Straße 6 - 4th floor seminar room|
The seminar starts with an introductory lecture to provide a compact overview of the research field (machine learning for medical image analysis), as well as a tutorial on critical analysis and presentation of research papers. Throughout the remainder of the course, each student presents two papers from a collection of seminal work in the field. Strong emphasis will be put on an engaging group discussion of the paper.
The learning objectives of this seminar consist of three parts: (1) the students will gain a solid understanding of key contributions to the field of machine learning for medical image analysis, (2) the students learn to critically read and analyse original research papers and judge their impact, and (3) the students will improve their scientific communication skills with an oral presentation and participation in discussions sessions.
- 20.10.: Paper selection and schedule is online
- 6.10.: We have reached full capacity! Please sign up for the waiting list though. There are usually a few people who unenroll before the semester starts, so you might still get a spot!
- 24.9.: Registration in ILIAS is now open
- 15.9.: Website is online
Please register for the course in ILIAS. Number of participants is limited to 12 students. We will maintain a waiting list - if you do not wish to take the course, please unenroll to let other people take your place.
Each student chooses two papers from the provided collection to present during the course of the seminar. The students may get support in the preparation of their presentation by the seminar assistants. Everybody is encouraged to read each paper before it is being presented and engage in a discussion following the presentations. To foster interesting discussions, each paper will also be assigned two “critics” who study the paper and prepare questions for the discussion. Each student will be graded based on both their presentation (80%) and their participation in the assigned discussions (20%). Attendance is required to pass the course (3 absences allowed).
Slides for the introductory lecture will be uploaded to ILIAS.
Schedule and List of Papers
Note: contact information of the TAs is provided in the course materials.
|Seminar date||Paper No.||Title||TA|
|22.10.2021||–||Introduction to course and to reading and presenting of scientific work||–|
|29.10.2021||–||Introduction to medical image analysis||–|
|Paper 1||U-Net: Convolutional Networks for Biomedical Image Segmentation (2015)||Sarah|
|5.11.2021||Paper 2||nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation (2021)||Nikolas|
|Paper 3||Integrating statistical prior knowledge into convolutional neural networks (2017)||Lisa|
|12.11.2021||Paper 4||Test-time adaptable neural networks for robust medical image segmentation (2020)||Indu|
|Paper 5||Fairness in cardiac mr image analysis: An investigation of bias due to data imbalance in deep learning based segmentation (2021)||Lisa|
|19.11.2021||Paper 6||[cancelled] ||Christian|
|Paper 7||Automatic anatomical brain MRI segmentation combining label propagation and decision fusion (2006)||Sarah|
|Paper 15||Attention-based Deep Multiple Instance Learning (2018)||Indu|
|26.11.2021||Paper 8||Voxelmorph: a learning framework for deformable medical image registration (2019)||Sarah|
|Paper 9||MR Image Reconstruction From Highly Undersampled k-Space Data by Dictionary Learning (2011)||Nikolas|
|3.12.2021||Paper 10||A Deep Cascade of Convolutional Neural Networks for Dynamic MR Image Reconstruction (2018)||Paul|
|Paper 11||Generative Adversarial Networks for Noise Reduction in Low-Dose CT (2017)||Stefano|
|10.12.2021||Paper 12||Learning a variational network for reconstruction of accelerated MRI data (2017)||Nikolas|
|Paper 13||Clinically applicable deep learning for diagnosis and referral in retinal disease (2018)||Stefano|
|17.12.2021||Paper 14||High-throughput adaptive sampling for whole-slide image analysis (HASHI) via convolutional neural networks: Application to invasive breast cancer detection (2018)||Paul|
|14.1.2022||Paper 16||DeepDose: Towards a fast dose calculation engine for radiation therapy using deep learning (2020)||Christian|
|Paper 17||Efficient Multi-Scale 3D CNN with Fully Connected CRF for Accurate Brain Lesion Segmentation (2016)||Sarah|
|21.1.2022||Paper 18||A probabilistic u-net for segmentation of ambiguous images (2018)||Nikolas|
|Paper 19||Towards safe deep learning: accurately quantifying biomarker uncertainty in neural network predictions (2018)||Paul|
|28.1.2022||Paper 20||f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks (2019)||Indu|
|Paper 21||[cancelled] ||Paul|
|4.2.2022||Paper 22||A bi-ventricular cardiac atlas built from 1000+ high resolution MR images of healthy subjects and an analysis of shape and motion (2015)||Lisa|
|Paper 23||A multi-modal parcellation of human cerebral cortex (2016)||Christian|
|11.2.2022||Paper 24||Causality matters in medical imaging (2020)||Indu|