Hauptseminar/Master Seminar: Deep Learning for Medical Applications

Prof. Nassir Navab, Shadi Albarqouni, Sailesh Conjeti

Type: Advanced Seminar Course Module IN8901
Type: Master Seminar Module IN2107
SWS: 2+0
ECTS: 4 Credits
Location: Seminar room 03.13.010
Time: 14-16 (tentative)
Course Language: English

Announcements

• 19-01-2017: Submission Deadline for both Report and Presentation is 31st January 2017 at 23:59.
• 02-11-2016: Date Assignment Complete: Please check your time slot.
• 28-10-2016: Paper Assignment (Complete). Please approach your tutor by email if you have any question!
• 25-10-2016: We have a meeting tomorrow to assign the papers.
• 05-08-2016: Preference List: Students are kindly asked to submit their preferences (3 topics) to Shadi Albarqouni starting from 17 October 2016 till 25 October 2016.
• 05-08-2016: Uptaing the topic list: We may update the list after the MICCAI Conference in October 2016.
• 04-07-2016: Registration is open from 01.07.2016 to 06.07.2016 through the TUM Matching Platform.
• 29-06-2016: Tentative Schedule is up!
• 29-06-2016: The list of topics is up!
• 29-06-2016: An introductory meeting: Wednesday, 29 June 2016 at 11 AM (30 minutes) in CAMP Seminar Room, 03.13.010.

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

• It should be performed between 01.07.2016 and 06.07.2016 through the TUM Matching Platform.

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 camp-tum-jhu-slides.zip, or Latex: CAMP-latex-template.zip.
• Written Report: A document of maximum 2 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.

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 (Tentative)

Date	Time	Topic	Slides	Students
2016-06-29	11:00	Preliminary meeting	Slides
2016-10-26	14-16	Intro. CNN Lecture (Tentative)/Paper Assignment
2016-11-23	14-16	Presentations		Haris Jabbar, Matthias Kühne, Dominik Durner
2016-11-30	14-16	Presentations		Moustafa Aboulatta, Emerson Tucker
2016-12-14	14-16	Presentations		Ahmed El-Gazzar, Tamoor Ahmad, Sara Haj
2017-12-21	14-16	Presentations		Atanas Mirchev, Anna Listsyna, Faisal Kalim
2017-01-11	14-16	Presentations		Sanjeev Kumar, Quniji Zhou, Paulius Sukys
2017-01-18	14-16	Presentations		Georgi Dikov, Mihai Baltac, Josue Page

List of Topics and Material

The list of papers:

No	Application	Title	Conference/Journal	Tutor	Student	Link
1	Lesion Segmentation	Parallel Multi-Dimensional LSTM, With Application to Fast Biomedical Volumetric Image Segmentation	NIPS-15	Felix	Ahmed El-Gazzar	Paper
2	-	Deep 3D Convolutional Encoder Networks With Shortcuts for Multiscale Feature Integration Applied to Multiple Sclerosis Lesion Segmentation	TMI-SIDL-16			Paper
3	-	V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation	arXiv	Shadi		arXiv
4	Lesion Detection	Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning	TMI-SIDL-16	Shadi	Sanjeev Kumar	Paper, arXiv
5	-	Improving Computer-aided Detection using Convolutional Neural Networks and Random View Aggregation	TMI-SIDL-16	Shadi	Emerson Tucker	Paper, arXiv
6	-	1) Iterative Multi-domain Regularized Deep Learning for Anatomical Structure Detection and Segmentation from Ultrasound Images 2) Automatic polyp detection in colonoscopy videos using an ensemble of convolutional neural networks	MICCAI-16, ISBI-15			Paper1 Paper2
7	-	Computer-Aided Diagnosis with Deep Learning Architecture: Applications to Breast Lesions in US Images and Pulmonary Nodules in CT Scans	nature-16	Diana	Quniji Zhou	Paper
8	Learning Methodology	Combining Generative and Discriminative Representation Learning for Lung CT Analysis With Convolutional Restricted Boltzmann Machines	TMI-SIDL-16		Georgi Dikov	Paper
9	-	Unsupervised Deep Learning Applied to Breast Density Segmentation and Mammographic Risk Scoring	TMI-SIDL-16	Sailesh	Tamoor Ahmad	Paper
10	Transfer Learning & Fine Tuning	1) Off-the-shelf convolutional neural network features for pulmonary nodule detection in computed tomography scans 2) Chest pathology detection using deep learning with non-medical training	ISBI-15	Abhijit	Paulius Sukys	Paper1 Paper2
11	Training Data Consideration	Fast Convolutional Neural Network Training Using Selective Data Sampling: Application to Hemorrhage Detection in Color Fundus Images	TMI-SIDL-16	Sailesh	Matthias Kühne	Paper
12	Annotation	Learning to Read Chest X-Rays: Recurrent Neural Cascade Model for Automated Image Annotation	CVPR-16	Ahmad	Moustafa Aboulatta	Paper, arXiv
13	-	Interleaved Text/Image Deep Mining on a Large-Scale Radiology Database	CVPR-15, JMLR	Ahmad	Mihai Baltac	Paper, arXiv
14	Crowdsourcing	AggNet: Deep Learning From Crowds for Mitosis Detection in Breast Cancer Histology Images	TMI-SIDL-16	Shadi		Paper
15	Registration	A CNN Regression Approach for Real-Time 2D/3D Registration	TMI-SIDL-16	Sailesh	Faisal Kalim	Paper
16	-	Pixel Recurrent Neural Networks	ICML-16	Ahmad	Atanas Mirchev	arXiv
17	-	Standard Plane Localization in Fetal Ultrasound via Domain Transferred Deep Neural Networks	JBHI-15	Abhijit	Dominik Durner	Paper
18	Surgical Workflow	EndoNet?: A Deep Architecture for Recognition Tasks on Laparoscopic Videos	arXiv (TBA)	Sailesh	Josue Page	arXiv
19	-	TSC-DL: Unsupervised Trajectory Segmentation of Multi-Modal Surgical Demonstrations with Deep Learning	ICRA-16			Paper
20	-	Query-by-example surgical activity detection	IJCARS-16	Felix	Sara Haj	Paper
21	Similarity Learning	Deep similarity learning for multimodal medical images	DLMA	Diana	Anna Listsyna	Paper
22		HeMIS?: Hetero-Modal Image Segmentation	MICCAI-16	Shadi	Haris Jabbar	arXiv

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:

• 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.