TeachingSs17DLMA

Chair for Computer Aided Medical Procedures & Augmented Reality
Lehrstuhl für Informatikanwendungen in der Medizin & Augmented Reality

Hauptseminar/Master Seminar: Deep Learning for Medical Applications

Prof. Nassir Navab, Shadi Albarqouni, Christoph Baur

Type: Advanced Seminar Course Module IN8901
Type: Master Seminar Module IN2107
SWS: 2+0
ECTS: 5 Credits
Location: CAMP Seminar Room, 03.13.010
Time: 12:30 - 14:00 (except for the introductory meeting!)
Course Language: English

Announcements

  • 29-05-2017: You can submit/push your report here. Please request an access first. Deadline is 28th July 2017 at 23:59.
  • 11-04-2017: Papers are assigned to the tutors. Please contact your tutor (latest 2 weeks before your presentation) to discuss with her/him the structure of your presentation.
  • 03-04-2017: Please send us your preferences (at least three papers) by Monday, 10th April 2017.
  • 01-02-2017: If you are still interested in the seminar, please write a brief motivation paragraph (few lines) showing your interest and your background in Machine/Deep Learning. Send this email, with a title “DLMA_Motivation”, to Shadi Albarqouni (shadi.albarqouni@tum.de) till 08.02.2017
  • 20-01-2017: Registration is open from 03.02.2017 to 08.02.2017 through the TUM Matching Platform.
  • 20-01-2017: Tentative Schedule is up!
  • 18-01-2017: An introductory meeting: Wednesday, 1 February 2017 at 2 PM (30 minutes) in CAMP Seminar Room, 03.13.010.
  • 09-01-2017: Website is up!

Introduction

  • Deep Learning is growing tremendously in Computer Vision and Medical Imaging as well. Highly impacted journals in medical imaging community, i.e. IEEE Transaction on Medical Imaging, published recently 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.

Registration

Requirements

In this Master Seminar (formerly Hauptseminar), each student is asked to send three preferences from the list, then he will be assigned one paper. 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 camp-tum-jhu-slides.zip, or Latex: CAMP-latex-template.zip.
  • Written Report: A document of maximum 2 pages should be submitted before the deadline. A link to the shared repository will be announced later.
  • Attendance: Participants have to participate actively in all seminar sessions.

The students are required to attend each seminar presentation which will be held during this course. Each presentation is followed by a discussion and everyone is encouraged to actively participate. The report must include all references used and must be written completely in your own words. Copy and paste will not be tolerated. Both report and presentation have to be done in English.

Schedule

Date Time Topic Slides Students
01.02.2017 14:00 Introductory meeting Slides Students who join this meeting and show their interest will have high priority!
27.04.2017 12:30 - 14:00 Kick-Off Session Review CNN (), RNN (Gerome/Ahmad), Autoencoders (Abhijit)
11.05.2017 12:30 - 14:00 Presentation Session 1   Ghazal, Ishmeet, Taha
18.05.2017 12:30 - 14:00 Presentation Session 2   Agnieszka, Jens, Yatin
01.06.2017 12:30 - 14:00 Presentation Session 3   Tomislav, Daniel, Anil
08.06.2017 12:30 - 14:00 Presentation Session 4   Evangelos, Edward, Fang
06.07.2017 12:30 - 14:00 Presentation Session 5   Cristian Ioan, Mustafa Taha
13.07.2017 12:30 - 14:00 Presentation Session 6   Can, Anne-Marie, Abu Rasiuddin

List of Topics and Material

The list of papers:

No Application Title Conference/Journal Tutor Student Link
1 CNN/FCN Dermatologist-level classification of skin cancer with deep neural networks Nature Shadi Ghazal HTML
2 - Deep learning as a tool for increased accuracy and efficiency of histopathological diagnosis Nature Marie Ishmeet HTML
3 - CT Image Denoising with Perceptive Deep Neural Networks arXiv Christoph Taha PDF
4 - CNN-based Segmentation of Medical Imaging Data arXiv Marie Evangelos PDF
5 - The One Hundred Layers Tiramisu: Fully Convolutional DenseNets? for Semantic Segmentation arXiv Patrick Can PDF
6 - Deep Image Retrieval: Learning global representations for image search ECCV 2016     PDF
Deep Learning Computed Tomography MICCAI 2016 Christoph Anne-Marie PDF
8 - Efficient Multi-Scale 3D CNN with Fully Connected CRF for Accurate Brain Lesion Segmentation arXiv Ahmad Edward PDF
9 - DeepNAT?: Deep Convolutional Neural Network for Segmenting Neuroanatomy arXiv Ahmad Fang PDF
10 RNN/LSTM Parallel Multi-Dimensional LSTM, With Application to Fast Biomedical Volumetric Image Segmentation NIPS Patrick Abu Rasiuddin PDF
11 - Combining Fully Convolutional and Recurrent Neural Networks for 3D Biomedical Image Segmentation NIPS Patrick Cristian Ioan PDF
12 - Prospective identification of hematopoietic lineage choice by deep learning Nature Tingying   Preview Link
13 - Recurrent Models of Visual Attention NIPS Ahmad Mustafa Taha PDF
14 - Phased LSTM: Accelerating Recurrent Network Training for Long or Event-based Sequences NIPS     PDF
15 GANs Adversarial Networks for the Detection of Aggressive Prostate Cancer arXiv Sailesh Tomislav PDF
16 - Medical Image Synthesis with Context-Aware Generative Adversarial Networks arXiv     PDF
17 - Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery IPMI Christoph Daniel PDF
18 - Unsupervised domain adaptation in brain lesion segmentation with adversarial networks IPMI     PDF
19 - (Unsupervised*) Image-to-Image Translation with Conditional Adversarial Nets arXiv Shadi Anil PDF (PDF*)
20 SAE/CAE/VAE Deep Patient: An Unsupervised Representation to Predict the Future of Patients from the Electronic Health Records Nature Shadi Agnieszka HTML
21 - Medical image denoising using convolutional denoising autoencoders arXiv Marie Yatin PDF
22 - Manifold Learning with Variational Auto-encoder for Medical Image Analysis UNC Christoph Jens PDF
23 RL An artificial agent for anatomical landmark detection in medical images MICCAI 2016     PDF
24 - An Artificial Agent for Robust Image Registration AAAI 2017     PDF


ICML: International Conference on Machine Learning
TMI-SIDL: IEEE Transaction on Medical Imaging, Special Issue on Deep Learning
ISBI: International Symposium on Biomedical Imaging
NIPS: Neural Information Processing Systems
ICRA: International Conference on Robotics and Automation
IJCARS: International Journal on Computer Assisted Radiology and Surgery
JBHI: IEEE Journal of Biomedical and Health Informatics
MICCAI: Medical Image Computing and Computer Assisted Intervention

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:

Some publishers:

Libraries (online and offline):

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.


TeachingForm
Title: Deep Learning for Medical Applications
Professor: Prof. Nassir Navab
Tutors: Shadi Albarqouni, Christoph Baur
Type: Hauptseminar
Information: Hauptseminar, SWS: 2, ECTS: 5
Term: 2017SoSe
Abstract:  


Edit | Attach | Refresh | Diffs | More | Revision r1.52 - 10 Jun 2017 - 12:57 - ShadiAlbarqouni

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