About this Research Topic
The development of intelligent data, image and video analysis systems has experienced a significant boost in recent years thanks to the emergence of a machine learning paradigm known as deep learning (DL) and the availability of low cost hardware to run neural networks efficiently. DL algorithms have enabled the development of highly accurate systems (with performance comparable to that of human experts, in some cases) and have become a standard choice for analyzing images. Dozens of commercial applications using deep learning to analyze, classify, segment, measure, and recognize contents from different modalities of data are currently available. Researchers in industry, hospitals, public sector and academia have published hundreds of scientific contributions in this area during the last year alone.
This Research Topic “Deep Learning: Status, Applications and Algorithms” provides a forum for the discussion of the impact of deep learning on data analysis and a focused issue for sharing novel scientific contributions in the area of deep learning.
Topic of interest include (but are not limited to):
· Novel approaches for classification, object classification, localization, object detection, segmentation and registration using DL;
· Content-Based Retrieval (CBIR) of data (sensors, text, images, video, etc.) using DL;
· Content understanding using DL;
· Data generation, fusion and enhancement methods using DL;
· Multimodal analysis using DL;
· Applications of DL
Authors are welcome to submit their original contributions before the deadline following the submission guidelines.
Topic Editor Prof Pal Halvorsen is also associated with ForzaSys AS. Topic Editor Prof. Tavanapong is the CTO and has an equity interest in EndoMetric Corporation. All other Topic Editors declare no competing interests with regards to the Research Topic subject.
Keywords: deep learning, algorithms, applied deep learning
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.