Instructors: Prof. Dr. Nassir Navab, Dr. Shahrooz Faghihroohi, Azade Farshad, Yousef Yeganeh
Time: TBA
Registration
- Registration must be done through TUM Matching Platform (please pay attention to the Deadlines)
- In order to increase your priority, please also apply via our own Registration system.
- The maximum number of participants: 20.
Announcements
- The presentation and blogpost guidelines are available here: Guide_DLMA SS2022.pdf
- The preliminary meeting slides can be found here: DLMAWS23-24.pdf
- The preliminary meeting is scheduled for July 5th, 14:30 (Zoom link is visible on TUMonline).
Introduction
- Deep Learning is growing tremendously in Computer Vision and Medical Imaging as well. Highly impacted journals in the medical imaging community, i.e. IEEE Transaction on Medical Imaging, recently published their special edition on Deep Learning [1]. The Seminar will propose a list of recent scientific articles related to the main current research topics in deep learning for Medical Applications, together with some interesting papers from other communities (CVPR, NeurIPS, ICCV, ICLR, ICML, ...).
Course Structure
In this Master Seminar (Hauptseminar), students select one scientific topic from the list provided by course organizers. The students should read the proposed sample papers by the tutors, find the topic-related articles, summarize and compare them in their presentation and blogpost:
- Presentation: The selected paper is presented to the other participants (Maximum 25 minutes presentation, 10 minutes questions). You can use the CAMP templates for PowerPoint TUM-Template.pptx.
- Blog Post: A blog post of 2500-3000 words excluding references, should be submitted before the deadline. The blog post must include all references used and must be written completely in your own words. Copy and paste will not be tolerated.
- Attendance: Participants have to participate actively in all seminar sessions. Each presentation is followed by a discussion, and everyone is encouraged to actively participate.
Submission Deadline: You have to submit the blog post by the last session.
Schedule (TBA)
| Date | Session: Topics | Students |
|---|---|---|
| 14.12 | Video Object Segmentation Audio Segmentation and Sound Event Detection Temporal Modeling for Longitudinal Medical Data | Zong, Xia Sengün Gökce Ruochen Li |
| 21.12 | Multimodal Learning with Functional and Structural MRI Analysis Epileptic Seizure Detection and Prediction Using EEG Learning-based Statistical Shape Model | El Alaoui Talibi, Ghita Seidl, Máté Tang, Yilin |
| 11.01 | Synthetic vessel generation 3D reconstruction from a single or biplanar images Automatic C-arm Positioning/Pose estimation Image Stitching Using Unsupervised/Semi-Supervised Learning | Obelleiro-Liz, Manuel Jingtian Zhao Ding Zhou Krüger, Moritz |
| 18.01 | Physics-inspired Neural Networks for Medical Applications Physics-inspired diffusion model Representation Learning for Modeling Interactions | Aggarwal, Kunal Weixuan Yuan Chia-Chian Chan |
| 25.01 | Rethinking ultrasound confidence maps Leveraging Knowledge for Medical Image Understanding in Radiology Temporal Knowledge Graphs | Baller, Stephan Khattab, Muhammad Fatemeh Shamsoddini Ardekani |
List of Topics and Material
The proposed papers for each topic in this course are usually selected from the following venues/publications:
CVPR: Conference on Computer Vision and Pattern Recognition
ICLR: International Conference on Learning Representations
NeurIPS: Neural Information Processing Systems
TPAMI: IEEE Transactions on Pattern Analysis and Machine Intelligence
TMI: IEEE Transaction on Medical Imaging
JBHI: IEEE Journal of Biomedical and Health Informatics
MedIA: Medical Image Analysis (Elsevier)
MICCAI: Medical Image Computing and Computer-Assisted Intervention
BMVC: British Machine Vision Conference
MIDL: Medical Imaging with Deep Learning
List of papers (TBA)
| No | Topic | Sample Papers | Journal/ Conference | Tutor | Student | Link |
|---|---|---|---|---|---|---|
| 1 | Representation Learning for Modeling Interactions | Physical Interaction: Reconstructing Hand-object Interactions with Physics | SIGGRAPH 2022 | Chia-Chian Chan | https://dl.acm.org/doi/abs/10.1145/3550469.3555421 | |
| What to look at and where: Semantic and Spatial Refined Transformer for detecting human-object interactions | CVPR 2022 | https://arxiv.org/pdf/2204.00746.pdf | ||||
| PIGNet: a physics-informed deep learning model toward generalized drug–target interaction predictions | Chemical Science 2022 | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8966633/ | ||||
| 2 | Multimodal Learning with Functional and Structural MRI Analysis | Structural and Functional MRI Data Differentially Predict Chronological Age and Behavioral Memory Performance | ENeuro 2022 | El Alaoui Talibi, Ghita | https://www.eneuro.org/content/9/6/ENEURO.0212-22.2022 | |
| Abnormal structural and functional network topological properties associated with left prefrontal, parietal, and occipital cortices significantly predict childhood TBI-related attention deficits: A semi-supervised deep learning study | Frontiers in Neuroscience 2023 | https://www.frontiersin.org/articles/10.3389/fnins.2023.1128646/full | ||||
| Combined Structural MR and Diffusion Tensor Imaging Classify the Presence of Alzheimer’s Disease With the Same Performance as MR Combined With Amyloid Positron Emission Tomography: A Data Integration Approach | Frontiers in Neuroscience 2022 | https://www.frontiersin.org/articles/10.3389/fnins.2021.638175/full | ||||
| 3 | Temporal Modeling for Longitudinal Medical Data | The Queensland Twin Adolescent Brain Project, a longitudinal study of adolescent brain development | Nature 2023 | Ruochen Li | https://www.nature.com/articles/s41597-023-02038-w | |
| LSOR: Longitudinally-Consistent Self-Organized Representation Learning | MICCAI 2023 | |||||
| Mixing Temporal Graphs with MLP for Longitudinal Brain Connectome Analysis | MICCAI 2023 | https://link.springer.com/chapter/10.1007/978-3-031-43895-0_73 | ||||
| 4 | Video Object Segmentation | Look Before You Match: Instance Understanding Matters in Video Object Segmentation | CVPR 2023 | Zong, Xia | ||
| Unsupervised video object segmentation via prototype memory network | CVPR 2023 | |||||
| GL-Fusion: Global-Local Fusion Network for Multi-view Echocardiogram Video Segmentation | MICCAI 2023 | https://arxiv.org/abs/2309.11144 | ||||
| 5 | Temporal Knowledge Graphs | Learning Meta-Representations of One-shot Relations for Temporal Knowledge Graph Link Prediction | IJCNN 2023 | Fatemeh Shamsoddini Ardekani | https://arxiv.org/pdf/2205.10621 | |
| Learning from History: Modeling Temporal Knowledge Graphs with Sequential Copy-Generation Networks | AAAI 2021 | https://ojs.aaai.org/index.php/AAAI/article/view/16604/16411 | ||||
| GenTKG: Generative Forecasting on Temporal Knowledge Graph | arXiv 2023 | https://arxiv.org/pdf/2310.07793 | ||||
| 6 | Image Stitching Using Unsupervised/Semi-Supervised Learning | Depth-Aware Multi-Grid Deep Homography Estimation with Contextual Correlation | IEEE Transactions on CSVT 2022 | Krüger, Moritz | https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9605632 | |
| Unsupervised Deep Image Stitching: Reconstructing Stitched Features to Images | TIP 2021 | https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9472883 | ||||
| Semi-supervised Deep Large-baseline Homography Estimation with Progressive Equivalence Constraint | AAAI 2023 | https://arxiv.org/abs/2212.02763 | ||||
| 7 | Learning-based Statistical Shape Model | Deep implicit statistical shape models for 3d medical image delineation | AAAI 2022 | Tang, Yilin | https://ojs.aaai.org/index.php/AAAI/article/view/20110 | |
| Deep Structural Causal Shape Models | ECCV 2022 | https://arxiv.org/abs/2208.10950 | ||||
| Leveraging unsupervised image registration for discovery of landmark shape descriptor | MedIA 2021 | https://www.sciencedirect.com/science/article/abs/pii/S1361841521002036 | ||||
| 8 | Audio Segmentation and Sound Event Detection | A review of deep learning techniques in audio event recognition (AER) applications | Multimedia Tools and Applications 2023 | Sengün Gökce | https://link.springer.com/article/10.1007/s11042-023-15891-z | |
| You Only Hear Once: A YOLO-like Algorithm for Audio Segmentation and Sound Event Detection | Applied Sciences 2022 | https://www.mdpi.com/2076-3417/12/7/3293 | ||||
| THE COCKTAIL FORK PROBLEM: THREE-STEM AUDIO SEPARATION FOR REAL-WORLD SOUNDTRACK | IEEE, ICASSP 2022 | https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9746005 | ||||
| 9 | Physics-inspired diffusion models | A Physics-informed Diffusion Model for High-fidelity Flow Field Reconstruction | Journal of Computational Physics 2022 | Weixuan Yuan | https://www.sciencedirect.com/science/article/pii/S0021999123000670 | |
| Physics-Driven Diffusion Models for Impact Sound Synthesis from Videos | CVPR 2023 | |||||
| PhysDiff: Physics-Guided Human Motion Diffusion Model | ICCV 2023 | https://nvlabs.github.io/PhysDiff | ||||
| 10 | Leveraging Knowledge for Medical Image Understanding in Radiology | KiUT: Knowledge-injected U-Transformer for Radiology Report Generation | CVPR 2023 | Chantal Pellegrini | Khattab, Muhammad | |
| Knowledge-enhanced Visual-Language Pre-training on Chest Radiology Images | Nature Communications, 2023 | https://www.nature.com/articles/s41467-023-40260-7 | ||||
| Cross-modal Prototype Driven Network for Radiology Report Generation | ECCV 2022 | https://link.springer.com/chapter/10.1007/978-3-031-19833-5_33 | ||||
| 11 | Physics-inspired Neural Networks for Medical Applications | Physics-informed neural networks for modeling physiological time series for cuffless blood pressure estimation | NPJ Digital Medicine (Nature) 2023 | Francesca De Benetti | Aggarwal, Kunal | https://www.nature.com/articles/s41746-023-00853-4 |
| WarpPINN: Cine-MR image registration with physics-informed neural networks. | MIA 2023 | https://arxiv.org/pdf/2211.12549.pdf | ||||
| Physics-Informed Neural Networks for Brain Hemodynamic Predictions Using Medical Imaging | TMI 2022 | https://ieeexplore.ieee.org/document/9740143 | ||||
| 12 | Rethinking ultrasound confidencce maps | ULTRASOUND CONFIDENCE MAPS OF INTENSITY AND STRUCTURE BASED ON DIRECTED ACYCLIC GRAPH AND ARTIFACT MODELS | ISBI 2021 | Baller, Stephan | https://arxiv.org/pdf/2011.11956.pdf | |
| Weakly Supervised Estimation of Shadow Confidence Maps in Fetal Ultrasound Imaging | TMI 2019 | https://arxiv.org/pdf/1811.08164v3.pdf | ||||
| Stochastic Neural Radiance Fields: Quantifying Uncertainty in Implicit 3D Representations | 3DV20121 | https://arxiv.org/pdf/2109.02123.pdf | ||||
| 13 | Synthetic vessel generation | VesselVAE: Recursive Variational Autoencoders for 3D Blood Vessel Synthesis | MICCAI 2023 | Agnieszka Tomczak | Obelleiro-Liz, Manuel | https://arxiv.org/pdf/2307.03592.pdf |
| Physiology-based simulation of the retinal vasculature enables annotation-free segmentation of OCT angiographs | MICCAI 2022 | https://arxiv.org/pdf/2207.11102.pdf | ||||
| Optional: Blood Vessel Geometry Synthesis using Generative Adversarial Networks | arxiv 2018 | https://arxiv.org/pdf/1804.04381.pdf | ||||
| 14 | Epileptic Seizure Detection and Prediction Using EEG | Efficient graph convolutional networks for seizure prediction using scalp EEG | Frontiers in NeuroScience 2022 | Seidl, Máté | https://www.frontiersin.org/articles/10.3389/fnins.2022.967116/full | |
| Patient-Specific Seizure Prediction via Adder Network and Supervised Contrastive Learning | IEEE Transactions on Neural System 2022 | https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9787538 | ||||
| Data Augmentation for Seizure Prediction with Generative Diffusion Model | Arxiv 2023 | https://arxiv.org/pdf/2306.08256.pdf | ||||
| 15 | 3D reconstruction from single image | Image-to-Graph Convolutional Network for Deformable Shape Reconstruction from a Single Projection Image | MICCAI 2021 | Jingtian Zhao | https://arxiv.org/pdf/2108.12533.pdf | |
| X2Vision : 3D CT Reconstruction from Biplanar X-Rays with Deep Structure Prior | MICCAI 2023 | https://link.springer.com/chapter/10.1007/978-3-031-43999-5_66 | ||||
| X2CT-GAN: Reconstructing CT from Biplanar X-Rays with Generative Adversarial Networks | CVPR 2019 | |||||
| 16 | Automatic C-arm Positioning/Pose estimation | Shape-Based Pose Estimation for Automatic Standard Views of the Knee | MICCAI2023 | Ding Zhou | https://link.springer.com/chapter/10.1007/978-3-031-43990-2_45 | |
| Aneurysm Pose Estimation with Deep Learning | MICCAI2023 | https://link.springer.com/chapter/10.1007/978-3-031-43895-0_51 | ||||
| C-arm positioning for standard projections during spinal implant placement | MIA2022 | https://www.sciencedirect.com/science/article/pii/S136184152200202X |
Literature and Helpful Links
A lot of scientific publications can be found online.
The following list may help you to find some further information on your particular topic:
- Microsoft Academic Search
- Google Scholar
- CiteSeer
- CiteULike
- Collection of Computer Science Bibliographies
Some publishers:
- ScienceDirect (Elsevier Journals)
- IEEE Journals
- ACM Digital Library
Libraries (online and offline):
- http://rzblx1.uni-regensburg.de/ezeit/ (Elektronische Zeitschriften Bibliothek)
- Verbundkatalog des Bibliotheksverbundes Bayern (BVB)
- Computer ORG
- http://www.ub.tum.de/ (TUM Library)
- To get access onto the electronic library, see http://www.ub.tum.de/medien/ejournals/readme.html
- "proxy.biblio.tu-muenchen.de" mit Port 8080 (nur fuer http). Damit klappen zumindest portal.acm.org und computer.org meistens
- Various proceedings of conferences in our AR-Lab, 03.13.036 (These proceedings are not for lending!)
Some further hints for working with references:
- JabRef is a Java program for comfortable working with Bibtex literature databases. Handy feature: if you know the PubMed ID for an article, JabRef can import data from there (via "Web Search/Medline").
- Mendeley is a cross-platform program for organising your references.
If you find useful resources that are not already listed here, please tell us, so we can add them for others. Thanks.