A transfer learning approach for classification of clinical significant prostate cancers from mpMRI scans

Quan Chen; Xiang Xu; Shiliang Hu; Xiao Li; Qing Zou; Yunpeng Li

doi:10.1117/12.2279021

A transfer learning approach for classification of clinical significant prostate cancers from mpMRI scans

Quan Chen
, Xiang Xu
, Shiliang Hu
, Xiao Li
, Qing Zou
, Yunpeng Li

Radiation Medicine

Producción científica: Conference contribution › revisión exhaustiva

36 Citas (Scopus)

Resumen

Deep learning has shown a great potential in computer aided diagnosis. However, in many applications, large dataset is not available. This makes the training of a sophisticated deep learning neural network (DNN) difficult. In this study, we demonstrated that with transfer learning, we can quickly retrain start-of-the-art DNN models with limited data provided by the prostateX challenge. The training data consists of 330 lesions, only 78 were clinical significant. Efforts were made to balance the data during training. We used ImageNet pre-trained inceptionV3 and Vgg-16 model and obtained AUC of 0.81 and 0.83 respectively on the prostateX test data, good for a 4th place finish. We noticed that models trained for different prostate zone has different sensitivity. Applying scaling factors before merging the result improves the AUC for the final result.

Idioma original	English
Título de la publicación alojada	Medical Imaging 2017
Subtítulo de la publicación alojada	Computer-Aided Diagnosis
Editores	Nicholas A. Petrick, Samuel G. Armato
ISBN (versión digital)	9781510607132
DOI	https://doi.org/10.1117/12.2279021
Estado	Published - 2017
Evento	Medical Imaging 2017: Computer-Aided Diagnosis - Orlando, United States Duración: feb 13 2017 → feb 16 2017

Serie de la publicación

Nombre	Progress in Biomedical Optics and Imaging - Proceedings of SPIE
Volumen	10134
ISSN (versión impresa)	1605-7422

Conference

Conference	Medical Imaging 2017: Computer-Aided Diagnosis
País/Territorio	United States
Ciudad	Orlando
Período	2/13/17 → 2/16/17

Nota bibliográfica

Publisher Copyright:
© 2017 SPIE.

ODS de las Naciones Unidas

Este resultado contribuye a los siguientes Objetivos de Desarrollo Sostenible

Good health and well being

ASJC Scopus subject areas

Electronic, Optical and Magnetic Materials
Atomic and Molecular Physics, and Optics
Radiology Nuclear Medicine and imaging
Biomaterials

Acceder al documento

10.1117/12.2279021

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Chen, Q., Xu, X., Hu, S., Li, X., Zou, Q., & Li, Y. (2017). A transfer learning approach for classification of clinical significant prostate cancers from mpMRI scans. En N. A. Petrick, & S. G. Armato (Eds.), Medical Imaging 2017: Computer-Aided Diagnosis Artículo 101344F (Progress in Biomedical Optics and Imaging - Proceedings of SPIE; Vol. 10134). https://doi.org/10.1117/12.2279021

@inproceedings{5c20a3972f6443d89b969e5bb4a20cab,

title = "A transfer learning approach for classification of clinical significant prostate cancers from mpMRI scans",

abstract = "Deep learning has shown a great potential in computer aided diagnosis. However, in many applications, large dataset is not available. This makes the training of a sophisticated deep learning neural network (DNN) difficult. In this study, we demonstrated that with transfer learning, we can quickly retrain start-of-the-art DNN models with limited data provided by the prostateX challenge. The training data consists of 330 lesions, only 78 were clinical significant. Efforts were made to balance the data during training. We used ImageNet pre-trained inceptionV3 and Vgg-16 model and obtained AUC of 0.81 and 0.83 respectively on the prostateX test data, good for a 4th place finish. We noticed that models trained for different prostate zone has different sensitivity. Applying scaling factors before merging the result improves the AUC for the final result.",

keywords = "Computer aided diagnosis (CADx), Convolution neural Network (CNN), Deep learning neural network (DNN), Multiparameter MRI (mpMRI), Prostate cancer, ProstateX challenge, Transfer learning, VGG",

author = "Quan Chen and Xiang Xu and Shiliang Hu and Xiao Li and Qing Zou and Yunpeng Li",

note = "Publisher Copyright: {\textcopyright} 2017 SPIE.; Medical Imaging 2017: Computer-Aided Diagnosis ; Conference date: 13-02-2017 Through 16-02-2017",

year = "2017",

doi = "10.1117/12.2279021",

language = "English",

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Chen, Q, Xu, X, Hu, S, Li, X, Zou, Q & Li, Y 2017, A transfer learning approach for classification of clinical significant prostate cancers from mpMRI scans. En NA Petrick & SG Armato (eds.), Medical Imaging 2017: Computer-Aided Diagnosis., 101344F, Progress in Biomedical Optics and Imaging - Proceedings of SPIE, vol. 10134, Medical Imaging 2017: Computer-Aided Diagnosis, Orlando, United States, 2/13/17. https://doi.org/10.1117/12.2279021

A transfer learning approach for classification of clinical significant prostate cancers from mpMRI scans. / Chen, Quan; Xu, Xiang; Hu, Shiliang et al.
Medical Imaging 2017: Computer-Aided Diagnosis. ed. / Nicholas A. Petrick; Samuel G. Armato. 2017. 101344F (Progress in Biomedical Optics and Imaging - Proceedings of SPIE; Vol. 10134).

Producción científica: Conference contribution › revisión exhaustiva

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AU - Xu, Xiang

AU - Hu, Shiliang

AU - Li, Xiao

AU - Zou, Qing

AU - Li, Yunpeng

PY - 2017

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N2 - Deep learning has shown a great potential in computer aided diagnosis. However, in many applications, large dataset is not available. This makes the training of a sophisticated deep learning neural network (DNN) difficult. In this study, we demonstrated that with transfer learning, we can quickly retrain start-of-the-art DNN models with limited data provided by the prostateX challenge. The training data consists of 330 lesions, only 78 were clinical significant. Efforts were made to balance the data during training. We used ImageNet pre-trained inceptionV3 and Vgg-16 model and obtained AUC of 0.81 and 0.83 respectively on the prostateX test data, good for a 4th place finish. We noticed that models trained for different prostate zone has different sensitivity. Applying scaling factors before merging the result improves the AUC for the final result.

AB - Deep learning has shown a great potential in computer aided diagnosis. However, in many applications, large dataset is not available. This makes the training of a sophisticated deep learning neural network (DNN) difficult. In this study, we demonstrated that with transfer learning, we can quickly retrain start-of-the-art DNN models with limited data provided by the prostateX challenge. The training data consists of 330 lesions, only 78 were clinical significant. Efforts were made to balance the data during training. We used ImageNet pre-trained inceptionV3 and Vgg-16 model and obtained AUC of 0.81 and 0.83 respectively on the prostateX test data, good for a 4th place finish. We noticed that models trained for different prostate zone has different sensitivity. Applying scaling factors before merging the result improves the AUC for the final result.

KW - Computer aided diagnosis (CADx)

KW - Convolution neural Network (CNN)

KW - Deep learning neural network (DNN)

KW - Multiparameter MRI (mpMRI)

KW - Prostate cancer

KW - ProstateX challenge

KW - Transfer learning

KW - VGG

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ER -

A transfer learning approach for classification of clinical significant prostate cancers from mpMRI scans

Resumen

Serie de la publicación

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ODS de las Naciones Unidas

ASJC Scopus subject areas

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