Cargando…
Interpretable Model Based on Pyramid Scene Parsing Features for Brain Tumor MRI Image Segmentation
Due to the black box model nature of convolutional neural networks, computer-aided diagnosis methods based on depth learning are usually poorly interpretable. Therefore, the diagnosis results obtained by these unexplained methods are difficult to gain the trust of patients and doctors, which limits...
Autores principales: | , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8820931/ https://www.ncbi.nlm.nih.gov/pubmed/35140806 http://dx.doi.org/10.1155/2022/8000781 |
_version_ | 1784646312460812288 |
---|---|
author | Zhao, Mingyang Xin, Junchang Wang, Zhongyang Wang, Xinlei Wang, Zhiqiong |
author_facet | Zhao, Mingyang Xin, Junchang Wang, Zhongyang Wang, Xinlei Wang, Zhiqiong |
author_sort | Zhao, Mingyang |
collection | PubMed |
description | Due to the black box model nature of convolutional neural networks, computer-aided diagnosis methods based on depth learning are usually poorly interpretable. Therefore, the diagnosis results obtained by these unexplained methods are difficult to gain the trust of patients and doctors, which limits their application in the medical field. To solve this problem, an interpretable depth learning image segmentation framework is proposed in this paper for processing brain tumor magnetic resonance images. A gradient-based class activation mapping method is introduced into the segmentation model based on pyramid structure to visually explain it. The pyramid structure constructs global context information with features after multiple pooling layers to improve image segmentation performance. Therefore, class activation mapping is used to visualize the features concerned by each layer of pyramid structure and realize the interpretation of PSPNet. After training and testing the model on the public dataset BraTS2018, several sets of visualization results were obtained. By analyzing these visualization results, the effectiveness of pyramid structure in brain tumor segmentation task is proved, and some improvements are made to the structure of pyramid model based on the shortcomings of the model shown in the visualization results. In summary, the interpretable brain tumor image segmentation method proposed in this paper can well explain the role of pyramid structure in brain tumor image segmentation, which provides a certain idea for the application of interpretable method in brain tumor segmentation and has certain practical value for the evaluation and optimization of brain tumor segmentation model. |
format | Online Article Text |
id | pubmed-8820931 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
spelling | pubmed-88209312022-02-08 Interpretable Model Based on Pyramid Scene Parsing Features for Brain Tumor MRI Image Segmentation Zhao, Mingyang Xin, Junchang Wang, Zhongyang Wang, Xinlei Wang, Zhiqiong Comput Math Methods Med Research Article Due to the black box model nature of convolutional neural networks, computer-aided diagnosis methods based on depth learning are usually poorly interpretable. Therefore, the diagnosis results obtained by these unexplained methods are difficult to gain the trust of patients and doctors, which limits their application in the medical field. To solve this problem, an interpretable depth learning image segmentation framework is proposed in this paper for processing brain tumor magnetic resonance images. A gradient-based class activation mapping method is introduced into the segmentation model based on pyramid structure to visually explain it. The pyramid structure constructs global context information with features after multiple pooling layers to improve image segmentation performance. Therefore, class activation mapping is used to visualize the features concerned by each layer of pyramid structure and realize the interpretation of PSPNet. After training and testing the model on the public dataset BraTS2018, several sets of visualization results were obtained. By analyzing these visualization results, the effectiveness of pyramid structure in brain tumor segmentation task is proved, and some improvements are made to the structure of pyramid model based on the shortcomings of the model shown in the visualization results. In summary, the interpretable brain tumor image segmentation method proposed in this paper can well explain the role of pyramid structure in brain tumor image segmentation, which provides a certain idea for the application of interpretable method in brain tumor segmentation and has certain practical value for the evaluation and optimization of brain tumor segmentation model. Hindawi 2022-01-31 /pmc/articles/PMC8820931/ /pubmed/35140806 http://dx.doi.org/10.1155/2022/8000781 Text en Copyright © 2022 Mingyang Zhao et al. https://creativecommons.org/licenses/by/4.0/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 Zhao, Mingyang Xin, Junchang Wang, Zhongyang Wang, Xinlei Wang, Zhiqiong Interpretable Model Based on Pyramid Scene Parsing Features for Brain Tumor MRI Image Segmentation |
title | Interpretable Model Based on Pyramid Scene Parsing Features for Brain Tumor MRI Image Segmentation |
title_full | Interpretable Model Based on Pyramid Scene Parsing Features for Brain Tumor MRI Image Segmentation |
title_fullStr | Interpretable Model Based on Pyramid Scene Parsing Features for Brain Tumor MRI Image Segmentation |
title_full_unstemmed | Interpretable Model Based on Pyramid Scene Parsing Features for Brain Tumor MRI Image Segmentation |
title_short | Interpretable Model Based on Pyramid Scene Parsing Features for Brain Tumor MRI Image Segmentation |
title_sort | interpretable model based on pyramid scene parsing features for brain tumor mri image segmentation |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8820931/ https://www.ncbi.nlm.nih.gov/pubmed/35140806 http://dx.doi.org/10.1155/2022/8000781 |
work_keys_str_mv | AT zhaomingyang interpretablemodelbasedonpyramidsceneparsingfeaturesforbraintumormriimagesegmentation AT xinjunchang interpretablemodelbasedonpyramidsceneparsingfeaturesforbraintumormriimagesegmentation AT wangzhongyang interpretablemodelbasedonpyramidsceneparsingfeaturesforbraintumormriimagesegmentation AT wangxinlei interpretablemodelbasedonpyramidsceneparsingfeaturesforbraintumormriimagesegmentation AT wangzhiqiong interpretablemodelbasedonpyramidsceneparsingfeaturesforbraintumormriimagesegmentation |