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Proactive Construction of an Annotated Imaging Database for Artificial Intelligence Training
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- Bivik Stadler, Caroline, 1986- (author)
- Linköpings universitet,Avdelningen för diagnostik och specialistmedicin,Medicinska fakulteten,Centrum för medicinsk bildvetenskap och visualisering, CMIV
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- Lindvall, Martin (author)
- Linköpings universitet,Medie- och Informationsteknik,Tekniska fakulteten,Sectra AB, Tekn Ringen 20, SE-58330 Linkoping, Sweden
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- Lundström, Claes (author)
- Linköpings universitet,Medie- och Informationsteknik,Tekniska fakulteten,Centrum för medicinsk bildvetenskap och visualisering, CMIV,Sectra AB, Tekn Ringen 20, SE-58330 Linkoping, Sweden
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- (creator_code:org_t)
- 2020-11-09
- 2021
- English.
- In: Journal of digital imaging. - : Springer-Verlag New York. - 0897-1889 .- 1618-727X. ; 34, s. 105-115
- Journal article (peer-reviewed)
Abstract
Subject headings
Close
- Artificial intelligence (AI) holds much promise for enabling highly desired imaging diagnostics improvements. One of the most limiting bottlenecks for the development of useful clinical-grade AI models is the lack of training data. One aspect is the large amount of cases needed and another is the necessity of high-quality ground truth annotation. The aim of the project was to establish and describe the construction of a database with substantial amounts of detail-annotated oncology imaging data from pathology and radiology. A specific objective was to be proactive, that is, to support undefined subsequent AI training across a wide range of tasks, such as detection, quantification, segmentation, and classification, which puts particular focus on the quality and generality of the annotations. The main outcome of this project was the database as such, with a collection of labeled image data from breast, ovary, skin, colon, skeleton, and liver. In addition, this effort also served as an exploration of best practices for further scalability of high-quality image collections, and a main contribution of the study was generic lessons learned regarding how to successfully organize efforts to construct medical imaging databases for AI training, summarized as eight guiding principles covering team, process, and execution aspects.
Subject headings
- MEDICIN OCH HÄLSOVETENSKAP -- Klinisk medicin -- Radiologi och bildbehandling (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Clinical Medicine -- Radiology, Nuclear Medicine and Medical Imaging (hsv//eng)
Keyword
- Artificial intelligence; Annotation; Case collection; Radiology; Pathology
Publication and Content Type
- ref (subject category)
- art (subject category)
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- Treanor, Darren
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- Forsberg, Daniel
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