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DeepLearning CAD 2018 : Deep Learning for Computer-Aided Diagnosis (CAD) systems- IPAS 2018- Special Session


Link: http://Deep learning for Computer-Aided Diagnosis (CAD) systems
When Dec 12, 2018 - Dec 14, 2018
Where Nice- France
Submission Deadline Sep 11, 2018
Notification Due Oct 15, 2018
Final Version Due Oct 30, 2018
Categories    deep learning   machine learning   medical imaging   CAD

Call For Papers

Call for Special Session Papers

Deep learning for Computer-Aided Diagnosis (CAD) systems

Mr. Ramzi GUETARI, ISI, Tunis El Manar University, Mme. Nawres KHLIFA, ISTMT, Tunis El Manar University,

Third IEEE International Conference on Image Processing, Applications and Systems (IPAS 2018)
12-14 December 2018, University of Nice Sophia Antipolis, France

Computer-aided diagnosis (CAD) has become one of the major research subjects in medical imaging. It enables early disease detection and robust diagnosis decision. CAD systems are more and more developed in order to help and improve the diagnosis, to overcome the limits of the human analysis, and to detect the pathological changes which cannot be sometimes detected by doctors.

CAD systems require the extraction of the most significant features to recognize or classify data. However, feature extraction is a difficult and boring task. Deep learning based systems overcome this limitation by extracting automatically relevant features. Indeed, it allows using directly the whole image and avoids the search for the best representation. Importantly, a deep learning process can learn which features to optimally place in which level on its own.

This special session is focused on the application of deep learning methods in medical images analysis and CAD systems development.

The aim of this special session is to attract researchers interested in CAD development using deep learning. Both theoretical and application contributions will be entertained.


Paper Submission September 11, 2018

Notification of acceptance October 15, 2018

Final Decision October 30, 2018

The session will seek original work on the following aspects but not limited to:

ยท Deep learning for:

- Tumor cell analysis

- Deformation detection

- Tissue Image Classification

- Texture Analysis

- Nodule classification

More information about the Conference including details on the submission process and authors kit is available at

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