WS 22/23 - Machine Learning for Medical Image Analysis (Seminar)
| Course title: | Machine Learning for Medical Image Analysis |
| Course ID: | ML-4506 |
| Semester: | Winter semester 2022/2023 |
| Lecturers: | Dr. Lisa Koch, Dr. Christian Baumgartner |
| Teaching assistants: | Paul Fischer, Kerol Djoumessi, Sarah Müller, Susu Sun |
| Time: | Thursday, 12:00 – 14:00 |
| Location: | Maria-von-Linden-Straße 6 - 4th floor seminar room |
| Materials: | Available on ILIAS |
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, the students present 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.
News
- 20.10.: Detailed programme is available
- 12.10.: Room location, TA’s and info on course logistics announced
- 3.6.: Website is online
Important information
Please register for the course in ILIAS. The number of participants is limited to 12 students. We are maintaining a waiting list - if you do not wish to take the course, please unenroll to let other people take your place. Please come to the first lecture on 20 October 2022 (even if you are still on the waiting list). If you don’t show up as an enrolled student, your spot will be freed for someone on the waiting list.
The seminar will be held in-person. If you belong to a vulnerable group and require an individual solution, please reach out to us.
Slides and teaching materials
Detailed information and materials can be found on ILIAS, including slides for the introductory lectures.
Schedule and list of papers
Note: The TAs' email addresses are linked above.
| Seminar date | Slot No. | Title | |
|---|---|---|---|
| 20.10.2022 | - | Introduction to course and to reading and presenting of scientific work | |
| 27.10.2022 | - | Introduction to medical image analysis | |
| 3.11.2022 | 1 | nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation (2021) | |
| 2 | Efficient Multi-Scale 3D CNN with Fully Connected CRF for Accurate Brain Lesion Segmentation (2016) | ||
| 10.11.2022 | 3 | Test-time adaptable neural networks for robust medical image segmentation (2020) | |
| 4 | Synthseg: Domain randomisation for segmentation of brain mri scans of any contrast and resolution (2021) | ||
| 17.11.2022 | 5 | A probabilistic u-net for segmentation of ambiguous images (2018) | |
| 6 | Transformer-based out-of-distribution detection for clinically safe segmentation (2022) | ||
| 24.11.2022 | 7 | Tutorial: image registration | |
| 8 | Voxelmorph: a learning framework for deformable medical image registration (2019) | ||
| 1.12.2022 | 9 | Implicit Neural Representations for Deformable Image Registration (2022) | |
| 10 | Tutorial: image reconstruction | ||
| 8.12.2022 | 11 | A Deep Cascade of Convolutional Neural Networks for Dynamic MR Image Reconstruction (2018) | |
| 12 | Learning a variational network for reconstruction of accelerated MRI data (2017) | ||
| 15.12.2022 | 13 | Robust Compressed Sensing MRI with Deep Generative Priors (2021) | |
| 14 | Generative Adversarial Networks for Noise Reduction in Low-Dose CT (2017) | ||
| 22.12.2022 | 15 | Clinically applicable deep learning for diagnosis and referral in retinal disease (2018) | |
| 16 | DeepDose: Towards a fast dose calculation engine for radiation therapy using deep learning (2020) | ||
| holidays | |||
| 12.1.2023 | 17 | Assessing the Trustworthiness of Saliency Maps for Localizing Abnormalities in Medical Imaging (2021) | |
| 18 | Concept attribution: Explaining CNN decisions to physicians (2022) | ||
| 19.1.2023 | 19 | Big Self-Supervised Models Advance Medical Image Classification (2021) | |
| 20 | TINC: Temporally Informed Non-contrastive Learning for Disease Progression Modeling in Retinal OCT Volumes (2022) | ||
| 26.1.2023 | 21 | End-to-end privacy preserving deep learning on multi-institutional medical imaging (2021) | |
| 22 | Estimating Model Performance Under Domain Shifts with Class-Specific Confidence Scores (2022) | ||
| 2.2.2023 | 23 | Causality matters in medical imaging (2020) | |
| 24 | Validation and algorithmic audit of a deep learning system for the detection of proximal femoral fractures in patients in the emergency department: a diagnostic accuracy study (2022) | ||
| 9.2.2023 | - | Retrospective |