Prof. Nassir Navab
Machine Learning in Imaging and Visualization
, Benjamin Gutierrez Becker
, Loic Peter
, Chun-Hao Paul Huang
, Sailesh Conjeti
Advanced Seminar Course Module IN8901
Master Seminar Module IN2107
Seminar Module IN0014
Location: MI 03.13.010
- [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 email@example.com 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 can be performed through the TUM Online matching system.
- Write a short email indicating your interest on the course to firstname.lastname@example.org
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
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