Heuristic neural network approach in histological sections detection of hydatidiform mole
© 2019 Society of Photo-Optical Instrumentation Engineers (SPIE). A heuristic-based, multineural network (MNN) image analysis as a solution to the problematical diagnosis of hydatidiform mole (HM) is presented. HM presents as tumors in placental cell structures, many of which exhibit premalignant ph...
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th-cmuir.6653943832-679622020-04-02T15:12:35Z Heuristic neural network approach in histological sections detection of hydatidiform mole Patison Palee Bernadette Sharp Leonard Noriega Neil Sebire Craig Platt Medicine © 2019 Society of Photo-Optical Instrumentation Engineers (SPIE). A heuristic-based, multineural network (MNN) image analysis as a solution to the problematical diagnosis of hydatidiform mole (HM) is presented. HM presents as tumors in placental cell structures, many of which exhibit premalignant phenotypes (choriocarcinoma and other conditions). HM is commonly found in women under age 17 or over 35 and can be partial HM or complete HM. Appropriate treatment is determined by correct categorization into PHM or CHM, a difficult task even for expert pathologists. Image analysis combined with pattern recognition techniques has been applied to the problem, based on 15 or 17 image features. The use of limited data for training and validation set was optimized using a k-fold validation technique allowing performance measurement of different MNN configurations. The MNN technique performed better than human experts at the categorization for both the 15- and 17-feature data, promising greater diagnostic consistency, and further improvements with the availability of larger datasets. 2020-04-02T15:12:35Z 2020-04-02T15:12:35Z 2019-10-01 Journal 23294310 23294302 2-s2.0-85077498964 10.1117/1.JMI.6.4.044501 https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85077498964&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/67962 |
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Medicine Patison Palee Bernadette Sharp Leonard Noriega Neil Sebire Craig Platt Heuristic neural network approach in histological sections detection of hydatidiform mole |
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© 2019 Society of Photo-Optical Instrumentation Engineers (SPIE). A heuristic-based, multineural network (MNN) image analysis as a solution to the problematical diagnosis of hydatidiform mole (HM) is presented. HM presents as tumors in placental cell structures, many of which exhibit premalignant phenotypes (choriocarcinoma and other conditions). HM is commonly found in women under age 17 or over 35 and can be partial HM or complete HM. Appropriate treatment is determined by correct categorization into PHM or CHM, a difficult task even for expert pathologists. Image analysis combined with pattern recognition techniques has been applied to the problem, based on 15 or 17 image features. The use of limited data for training and validation set was optimized using a k-fold validation technique allowing performance measurement of different MNN configurations. The MNN technique performed better than human experts at the categorization for both the 15- and 17-feature data, promising greater diagnostic consistency, and further improvements with the availability of larger datasets. |
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Journal |
author |
Patison Palee Bernadette Sharp Leonard Noriega Neil Sebire Craig Platt |
author_facet |
Patison Palee Bernadette Sharp Leonard Noriega Neil Sebire Craig Platt |
author_sort |
Patison Palee |
title |
Heuristic neural network approach in histological sections detection of hydatidiform mole |
title_short |
Heuristic neural network approach in histological sections detection of hydatidiform mole |
title_full |
Heuristic neural network approach in histological sections detection of hydatidiform mole |
title_fullStr |
Heuristic neural network approach in histological sections detection of hydatidiform mole |
title_full_unstemmed |
Heuristic neural network approach in histological sections detection of hydatidiform mole |
title_sort |
heuristic neural network approach in histological sections detection of hydatidiform mole |
publishDate |
2020 |
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https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85077498964&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/67962 |
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