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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.

Proceedings will be published by SPRINGER under the “Lecture Notes in Computational Vision and Biomechanics” book series. The authors of the best papers presented will be invited to be included as extended versions in a special issue of the Taylor and Francis group “Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization” journal.


The goal of DLMIA is the presentation of works focused on the design and use of deep learning methods in medical image analysis applications. This workshop is going to set the trends and identify the challenges of the use of deep learning methods in medical image analysis. Another important objective of the workshop is to continue and increase the connection between software developers, specialist researchers and applied end-users from diverse fields related to Medical Image and Signal Processing.


  • 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.

ARC info 

This workshop is partially supported by Australian Research Council,
discovery project DP140102794 and ARC Future Fellowship (FT110100623).