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Semi-Supervised Learning in Medical Images Through Graph-Embedded Random Forest
One major challenge in medical imaging analysis is the lack of label and annotation which usually requires medical knowledge and training. This issue is particularly serious in the brain image analysis such as the analysis of retinal vasculature, which directly reflects the vascular condition of Cen...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7683389/ https://www.ncbi.nlm.nih.gov/pubmed/33240071 http://dx.doi.org/10.3389/fninf.2020.601829 |
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author | Gu, Lin Zhang, Xiaowei You, Shaodi Zhao, Shen Liu, Zhenzhong Harada, Tatsuya |
author_facet | Gu, Lin Zhang, Xiaowei You, Shaodi Zhao, Shen Liu, Zhenzhong Harada, Tatsuya |
author_sort | Gu, Lin |
collection | PubMed |
description | One major challenge in medical imaging analysis is the lack of label and annotation which usually requires medical knowledge and training. This issue is particularly serious in the brain image analysis such as the analysis of retinal vasculature, which directly reflects the vascular condition of Central Nervous System (CNS). In this paper, we present a novel semi-supervised learning algorithm to boost the performance of random forest under limited labeled data by exploiting the local structure of unlabeled data. We identify the key bottleneck of random forest to be the information gain calculation and replace it with a graph-embedded entropy which is more reliable for insufficient labeled data scenario. By properly modifying the training process of standard random forest, our algorithm significantly improves the performance while preserving the virtue of random forest such as low computational burden and robustness over over-fitting. Our method has shown a superior performance on both medical imaging analysis and machine learning benchmarks. |
format | Online Article Text |
id | pubmed-7683389 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-76833892020-11-24 Semi-Supervised Learning in Medical Images Through Graph-Embedded Random Forest Gu, Lin Zhang, Xiaowei You, Shaodi Zhao, Shen Liu, Zhenzhong Harada, Tatsuya Front Neuroinform Neuroscience One major challenge in medical imaging analysis is the lack of label and annotation which usually requires medical knowledge and training. This issue is particularly serious in the brain image analysis such as the analysis of retinal vasculature, which directly reflects the vascular condition of Central Nervous System (CNS). In this paper, we present a novel semi-supervised learning algorithm to boost the performance of random forest under limited labeled data by exploiting the local structure of unlabeled data. We identify the key bottleneck of random forest to be the information gain calculation and replace it with a graph-embedded entropy which is more reliable for insufficient labeled data scenario. By properly modifying the training process of standard random forest, our algorithm significantly improves the performance while preserving the virtue of random forest such as low computational burden and robustness over over-fitting. Our method has shown a superior performance on both medical imaging analysis and machine learning benchmarks. Frontiers Media S.A. 2020-11-10 /pmc/articles/PMC7683389/ /pubmed/33240071 http://dx.doi.org/10.3389/fninf.2020.601829 Text en Copyright © 2020 Gu, Zhang, You, Zhao, Liu and Harada. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Neuroscience Gu, Lin Zhang, Xiaowei You, Shaodi Zhao, Shen Liu, Zhenzhong Harada, Tatsuya Semi-Supervised Learning in Medical Images Through Graph-Embedded Random Forest |
title | Semi-Supervised Learning in Medical Images Through Graph-Embedded Random Forest |
title_full | Semi-Supervised Learning in Medical Images Through Graph-Embedded Random Forest |
title_fullStr | Semi-Supervised Learning in Medical Images Through Graph-Embedded Random Forest |
title_full_unstemmed | Semi-Supervised Learning in Medical Images Through Graph-Embedded Random Forest |
title_short | Semi-Supervised Learning in Medical Images Through Graph-Embedded Random Forest |
title_sort | semi-supervised learning in medical images through graph-embedded random forest |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7683389/ https://www.ncbi.nlm.nih.gov/pubmed/33240071 http://dx.doi.org/10.3389/fninf.2020.601829 |
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