Deep Learning in medicine is one of the most rapidly and new developing fields of science. Currently, almost every medical device intended for imaging has a more or less extended image and signal analysis and processing module which can use deep learning. It provides quantitative data necessary to make a diagnosis with predicting diagnosis. The obtained quantitative features must be independent of the inter-subject variability and the type of medical device and, above all, must allow for reproducible results in the presence of high noise. The proposed deep learning algorithms should also ensure the independence of the results obtained by the operator of the imaging device and, to be more exact, its position relative to the patient or the parameter settings in the device. In addition, the proposed deep learning algorithms must be tailored for the diagnosis of a specific disease entity. On the other hand, they must allow for reproducible results for high inter-subject variability. These criteria make it difficult to propose a methodology for the deep learning algorithms. This special issue is dedicated to this area of knowledge.
- Deep neural network in medical image processing (RTG, USG, CT, PET, OCT and others)
- New deep neural network architecture
- The use of applications with deep machine learning for recognizing objects in a 3D scene
- Deep machine learning in large data sets
- Deep robot learning
- Data mining with deep learning in bioinformatics
- Applications, algorithms, tools directly related to deep learning
How to submit:
Please submit your manuscript using the online submission system for the journal here. In submitting you manuscript, you should make it clear that your submission is part of this special issue. On the submission system there is a section entitled “Additional information”. At the bottom of the page there is a question asking “Are you submitting this manuscript to a thematic series or article collection?” You should select “Yes” and then a drop-down menu will appear with the option to select the special issue “Deep learning in biomedical engineering”.