Hauptseminar/Master Seminar:
Machine Learning in Imaging and Visualization
Prof. Nassir Navab,
Benjamin Gutierrez Becker,
Loic Peter,
Chun-Hao Paul Huang,
Sailesh Conjeti
Type: Advanced Seminar Course
Module IN8901
Type: Master Seminar
Module IN2107
Type: Seminar
Module IN0014
SWS: 2+0
ECTS: 4 Credits
Location: MI 03.13.010
Time: Wednesday 10:00-11:30
Course Language: English
Announcements
- [06.10.2014] The list of papers is now online. Please contact your supervisor as soon as possible.
- [01.10.2014] IMPORTANT!! Please send a ranked list of your 5 preferred papers at gutierrez.becker@tum.de before Friday 03.10.2114
- [25.06.2014] There will be a short preliminary meeting on Tuesday, July 1st, 2014 at 10:30 in 03.13.010.
Registration
- Registration can be performed through the TUM Online matching system.
- Write a short email indicating your interest on the course to gutierrez.becker@tum.de
Requirements
In this Master Seminar (formerly Hauptseminar), each student is asked to select one paper from a list. In order to successfully complete the seminar, participants have to fulfill these requirements:
- Presentation: The selected paper is presented to the other participants (20 minutes presentation 10 minutes questions). Use the CAMP templates for PowerPoint CAMPmaster.pot, or Latex: CAMP-latex-template.zip.
- Written Handout: A document of maximum 8 pages is written and submitted one week after the presentation. Please download and use the llncs2e.zip Latex template.
- Attendance: Participants have to participate actively in all seminar sessions.
Each presentation is followed by a discussion and everyone is encouraged to actively participate. The handout must include all references used and must be written
completely in your own words. Copy and paste will not be tolerated. Both handout and presentation have to be done in
English.
Schedule
List of Papers
*
A deep learning architecture for image representation, visual interpretability and automated basal-cell carcinoma cancer detection. , Cruz Roa et al, MICCAI 2013
*
Robust multimodal dictionary learning , Cao et al, MICCAI 2013
*
Sparse projections of medical images onto manifolds. , Chen et al, IPMI 2013
*
Towards realtime multimodal fusion for image-guided interventions using self-similarities. , Heinrich et al, MICCAI 2013
*
One-shot learning of anatomical structure localization models , Donner et al, ISBI 2013
*
Atlas encoding by randomized forests for efficient label propagation , Zikic et al, ICCV 2013
*
Predicting Cognitive Data from Medical Images Using Sparse Linear Regression , Kandel et al, IPMI 2013
*
Drosophila Embryo Stage Annotation using Label Propagation , Kazmar et al, ICCV 2013
*
Laplacian Forests: Semantic Image Segmentation by Guided Bagging , Lombaert et al, MICCAI 2014
*
Tell me what you see and I will show you where it is , Xu et al, CVPR 2014
*
Learning Without Labeling: Domain Adaptation for Ultrasound Transducer Localization , Heimann et al, MICCAI 2013
*
Human Body Shape Estimation Using a Multi-Resolution Manifold Forest, , Perbet et al, CVPR 2014
*
Model Transport: Towards Scalable Transfer Learning on Manifolds , Hauberg et al, CVPR 2014
*
Dense Non-Rigid Shape Correspondence Using Random Forests , Rodola et al, CVPR 2014
*
Latent Regression Forest: Structured Estimation of 3D Hand Posture , Tang et al, CVPR 2014
*
Incremental Learning of NCM Forests for Large-Scale Image Classification , Ristin et al, CVPR 2014
*
Joint Learning of Appearance and Transformation for Predicting Brain MR Image Registration , Wang et al, IPMI 2014
*
Robust and Accurate Shape Model Fitting Using Random Forest Regression Voting , Cootes et al, ECCV 2012
*
Precision-Recall-Classification Evaluation Framework: Application to Depth Estimation on Single Images , Palou et al, ECCV 2014
*
Shape Forest: Building constrained Statistical Shape Models with Decision Trees , Grbic et al, ECCV 2014
*
Depth-Based Shape Analysis Hong et al, MICCAI 2014
