RIL-Contour: a Medical Imaging Dataset Annotation Tool for and with Deep Learning.
Deep-learning algorithms typically fall within the domain of supervised artificial intelligence and are designed to "learn" from annotated data. Deep-learning models require large, diverse training datasets for optimal model convergence. The effort to curate these datasets is widely regarded as a ba...
| Publicado en: | Journal of Digital Imaging Vol. 32; no. 4; pp. 571 - 582 |
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| Autores principales: | , , , , , , , , , , |
| Formato: | diagnostic images tables/charts Journal Article |
| Publicado: |
Springer Nature
Aug2019
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=137642037&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 137642037 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Aug2019 vid: 32 iid: 4 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 137642037 137642037 137642037 10.1007/s10278-019-00232-0 137642037 ppf: 571 ppct: 11 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: RIL-Contour: a Medical Imaging Dataset Annotation Tool for and with Deep Learning. aug: au: Philbrick, Kenneth A. Weston, Alexander D. Akkus, Zeynettin Kline, Timothy L. Korfiatis, Panagiotis Sakinis, Tomas Kostandy, Petro Boonrod, Arunnit Zeinoddini, Atefeh Takahashi, Naoki Erickson, Bradley J. affil: Radiology Informatics Laboratory, Department of Radiology, Mayo Clinic, Rochester, MN, USA sug: subj: Deep Learning Software Diagnostic Imaging Radiologists Artificial Intelligence Algorithms Documentation ab: Deep-learning algorithms typically fall within the domain of supervised artificial intelligence and are designed to "learn" from annotated data. Deep-learning models require large, diverse training datasets for optimal model convergence. The effort to curate these datasets is widely regarded as a barrier to the development of deep-learning systems. We developed RIL-Contour to accelerate medical image annotation for and with deep-learning. A major goal driving the development of the software was to create an environment which enables clinically oriented users to utilize deep-learning models to rapidly annotate medical imaging. RIL-Contour supports using fully automated deep-learning methods, semi-automated methods, and manual methods to annotate medical imaging with voxel and/or text annotations. To reduce annotation error, RIL-Contour promotes the standardization of image annotations across a dataset. RIL-Contour accelerates medical imaging annotation through the process of annotation by iterative deep learning (AID). The underlying concept of AID is to iteratively annotate, train, and utilize deep-learning models during the process of dataset annotation and model development. To enable this, RIL-Contour supports workflows in which multiple-image analysts annotate medical images, radiologists approve the annotations, and data scientists utilize these annotations to train deep-learning models. To automate the feedback loop between data scientists and image analysts, RIL-Contour provides mechanisms to enable data scientists to push deep newly trained deep-learning models to other users of the software. RIL-Contour and the AID methodology accelerate dataset annotation and model development by facilitating rapid collaboration between analysts, radiologists, and engineers. pubtype: Academic Journal doctype: diagnostic images tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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