[visionlist] DLMIA 2015 – a MICCAI workshop

***********************************************CFP – DLMIA 2015 – a MICCAI workshop***********************************************

1st Workshop on Deep Learning in Medical Image Analysis (in conjunction with MICCAI 2015)

Call for Papers

Deep learning has produced promising results outperforming some state-of-the-art approaches for a couple of problems, such as face detection and recognition, speech recognition and image classification. It is expected that these algorithms can have a large impact on medical image analysis applications, such as computer-aided diagnosis, image segmentation, image annotation and retrieval, image registration and multimodal image analysis. However, only a few works have used deep learning methods in the context of medical-oriented applications, such as breast cancer and skin lesion detection, organs recognition and image-based disease identification.

Additionally, there is a little effort on model selection of deep learning techniques, which poses an interesting problem, since we may face hundreds of parameters, being a near-exhaustive search on this high-dimensional search space impractical. The problem gets worse in large image-based datasets, which have been commonly used in several recent papers. Given the large amount of parameters, some authors have argued that a random search may perform satisfactory well for some applications. However, a hand tuning of the parameters may limit our understanding about how well the techniques can generalize and describe data.

Deep Learning in Medical Image Analysis (DLMIA 2015) is the first workshop in conjunction with MICCAI 2015 that aims at fostering the area of computer-aided medical diagnosis, as well as meta-heuristic-based model selection concerning deep learning techniques.

Topics:

– Image description by means of deep learning techniques;

– Medical imaging-based diagnosis using deep learning;

– Medical signal-based diagnosis using deep learning;

– Medical image reconstruction using deep learning;

– Deep learning model selection;

– Meta-heuristic techniques for fine-tuning parameter in deep learning-based architectures;

– Deep learning-oriented applications.

Important Dates:

– Full Paper Submission: June 10th, 2015

– Notification of Acceptance: June 30th, 2015

– Camera-ready Version: July 10th, 2015

– Conference Date: October 5th, 2015 (TBD)

Invited Speaker: 

Juergen Schmidhuber, IDSIA, Switzerland

Website

http://ift.tt/1M8GsnB

Organization: 

Gustavo Carneiro, University of Adelaide

João Manuel R. S. Tavares, Universidade do Porto

Andrew P. Bradley, University of Queensland

João Paulo Papa, Universidade Estadual Paulista

Jacinto C. Nascimento, Instituto Superior Tecnico

Jaime S. Cardoso, Universidade do Porto

Zhi Lu, University of Adelaide

Vasileios Belagiannis, Technische Universität München

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