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Detecting Disease in Radiographs with Intuitive Confidence

This paper argues in favor of a specific type of confidence for use in computer-aided diagnosis and disease classification, namely, sine/cosine values of angles represented by points on the unit circle. The paper shows how this confidence is motivated by Chinese medicine and how sine/cosine values a...

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Autor principal: Jaeger, Stefan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi Publishing Corporation 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4606081/
https://www.ncbi.nlm.nih.gov/pubmed/26495433
http://dx.doi.org/10.1155/2015/946793
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author Jaeger, Stefan
author_facet Jaeger, Stefan
author_sort Jaeger, Stefan
collection PubMed
description This paper argues in favor of a specific type of confidence for use in computer-aided diagnosis and disease classification, namely, sine/cosine values of angles represented by points on the unit circle. The paper shows how this confidence is motivated by Chinese medicine and how sine/cosine values are directly related with the two forces Yin and Yang. The angle for which sine and cosine are equal (45°) represents the state of equilibrium between Yin and Yang, which is a state of nonduality that indicates neither normality nor abnormality in terms of disease classification. The paper claims that the proposed confidence is intuitive and can be readily understood by physicians. The paper underpins this thesis with theoretical results in neural signal processing, stating that a sine/cosine relationship between the actual input signal and the perceived (learned) input is key to neural learning processes. As a practical example, the paper shows how to use the proposed confidence values to highlight manifestations of tuberculosis in frontal chest X-rays.
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spelling pubmed-46060812015-10-22 Detecting Disease in Radiographs with Intuitive Confidence Jaeger, Stefan ScientificWorldJournal Research Article This paper argues in favor of a specific type of confidence for use in computer-aided diagnosis and disease classification, namely, sine/cosine values of angles represented by points on the unit circle. The paper shows how this confidence is motivated by Chinese medicine and how sine/cosine values are directly related with the two forces Yin and Yang. The angle for which sine and cosine are equal (45°) represents the state of equilibrium between Yin and Yang, which is a state of nonduality that indicates neither normality nor abnormality in terms of disease classification. The paper claims that the proposed confidence is intuitive and can be readily understood by physicians. The paper underpins this thesis with theoretical results in neural signal processing, stating that a sine/cosine relationship between the actual input signal and the perceived (learned) input is key to neural learning processes. As a practical example, the paper shows how to use the proposed confidence values to highlight manifestations of tuberculosis in frontal chest X-rays. Hindawi Publishing Corporation 2015 2015-10-01 /pmc/articles/PMC4606081/ /pubmed/26495433 http://dx.doi.org/10.1155/2015/946793 Text en Copyright © 2015 Stefan Jaeger. https://creativecommons.org/licenses/by/3.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Jaeger, Stefan
Detecting Disease in Radiographs with Intuitive Confidence
title Detecting Disease in Radiographs with Intuitive Confidence
title_full Detecting Disease in Radiographs with Intuitive Confidence
title_fullStr Detecting Disease in Radiographs with Intuitive Confidence
title_full_unstemmed Detecting Disease in Radiographs with Intuitive Confidence
title_short Detecting Disease in Radiographs with Intuitive Confidence
title_sort detecting disease in radiographs with intuitive confidence
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4606081/
https://www.ncbi.nlm.nih.gov/pubmed/26495433
http://dx.doi.org/10.1155/2015/946793
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