Nvidia best paper of DLMIA 2016 went to: Michal Drozdzal and Eugene Vorontsov and Gabriel Chartrand and Samuel Kadoury and Christopher Pal. The importance of skip connections in biomedical image segmentation.
We are very proud to announce that DLMIA 2016 will be sponsored by Nvidia and Butterfly Network. Nvidia will offer a GPU for the best paper prize while Butterfly Network will provide support for our invited speakers.
After the success of the 1st DLMIA, held with MICCAI 2015, we present the 2nd DLMIA to be held with MICCAI 2016. Deep learning methods have experienced an immense growth in interest from the medical image analysis community, particularly in the last two years. The main reasons behind this interest lie in ability of deep learning algorithms to process very large training sets, to transfer learned features between different databases, and to analyse multimodal data. These advantages are providing important opportunities for the development of medical image analysis methodologies, such as computer-aided diagnosis, image segmentation, image annotation and retrieval, image registration and multimodal image analysis. Deep Learning in Medical Image Analysis (DLMIA) is a workshop dedicated to the presentation of works focused on the design and use of deep learning methods in medical image analysis applications. We expect that this workshop is setting the trends and identifying 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, which are the main scopes of MICCAI.
Proceedings will be published by SPRINGER under the “Lecture Notes in Computer Science” book series.
The main objective of this workshop is to advance scientific research of deep learning methods in medical image analysis. The workshop is going to foster the debate within the medical image analysis community of the recently proposed deep learning methods, which can help advance the current state of the art, particularly in computer-aided diagnosis, image segmentation, image annotation and retrieval, image registration and multimodal image analysis. We also hope it brings the attention of the applied optimization research community.