Notable Papers and Trends from 2019 in Sensors, Signals, and Imaging Informatics
| Autor: | Hsu, William, Baumgartner, Christian, Deserno, Thomas M, Section Editors for the IMIA Yearbook Section on Sensors, Signals, and Imaging Informatics |
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| Rok vydání: | 2020 |
| Předmět: |
Diagnostic Imaging
Signals Neural Networks Computer Computer-Assisted Deep Learning Library and Information Studies Signal Processing Public Health and Health Services Humans and Imaging Informatics Biochemistry and Cell Biology Medical Informatics Section Editors for the IMIA Yearbook Section on Sensors |
| Zdroj: | Yearbook of medical informatics, vol 29, iss 1 |
| Popis: | ObjectiveTo highlight noteworthy papers that are representative of 2019 developments in the fields of sensors, signals, and imaging informatics.MethodA broad literature search was conducted in January 2020 using PubMed. Separate predefined queries were created for sensors/signals and imaging informatics using a combination of Medical Subject Heading (MeSH) terms and keywords. Section editors reviewed the titles and abstracts of both sets of results. Papers were assessed on a three-point Likert scale by two co-editors, rated from 3 (do not include) to 1 (should be included). Papers with an average score of 2 or less were then read by all three section editors, and the group nominated top papers based on consensus. These candidate best papers were then rated by at least six external reviewers.ResultsThe query related to signals and sensors returned a set of 255 papers from 140 unique journals. The imaging informatics query returned a set of 3,262 papers from 870 unique journals. Based on titles and abstracts, the section co-editors jointly filtered the list down to 50 papers from which 15 candidate best papers were nominated after discussion. A composite rating after review determined four papers which were then approved by consensus of the International Medical Informatics Association (IMIA) Yearbook editorial board. These best papers represent different international groups and journals.ConclusionsThe four best papers represent state-of-the-art approaches for processing, combining, and analyzing heterogeneous sensor and imaging data. These papers demonstrate the use of advanced machine learning techniques to improve comparisons between images acquired at different time points, fuse information from multiple sensors, and translate images from one modality to another. |
| Databáze: | OpenAIRE |
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