Automatic HCC Detection Using Convolutional Network with Multi-Magnification Input Images

Wei Che Huang, Pau Choo Chung, Hung Wen Tsai, Nan Haw Chow, Ying Zong Juang, Hann Huei Tsai, Shih Hsuan Lin, Cheng Hsiung Wang

研究成果: Conference contribution

摘要

Liver cancer postoperative pathologic examination of stained tissues is an important step in identifying prognostic factors for follow-up care. Traditionally, liver cancer detection would be performed by pathologists with observing the entire biological tissue, resulting in heavy work loading and potential misjudgment. Accordingly, the studies of the automatic pathological examination have been popular for a long period of time. Most approaches of the existing cancer detection, however, only extract cell level information based on single-scale high-magnification patch. In liver tissues, common cell change phenomena such as apoptosis, necrosis, and steatosis are similar in tumor and benign. Hence, the detection may fail when the patch only covered the changed cells area that cannot provide enough neighboring cell structure information. To conquer this problem, the convolutional network architecture with multi-magnification input can provide not only the cell level information by referencing high-magnification patches, but also the cell structure information by referencing low-magnification patches. The detection algorithm consists of two main structures: 1) extraction of cell level and cell structure level feature maps from high-magnification and low-magnification images respectively by separate general convolutional networks, and 2) integration of multi-magnification features by fully connected network. In this paper, VGG16 and Inception V4 were applied as the based convolutional network for liver tumor detection task. The experimental results showed that VGG16 based multi-magnification input convolutional network achieved 91% mIOU on HCC tumor detection task. In addition, with comparison between single-scale CNN (SSCN) and multi-scale CNN (MSCN) approaches, the MSCN demonstrated that the multi-scale patches could provide better performance on HCC classification task.

原文English
主出版物標題Proceedings 2019 IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2019
發行者Institute of Electrical and Electronics Engineers Inc.
頁面194-198
頁數5
ISBN(電子)9781538678848
DOIs
出版狀態Published - 2019 三月
事件1st IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2019 - Hsinchu, Taiwan
持續時間: 2019 三月 182019 三月 20

出版系列

名字Proceedings 2019 IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2019

Conference

Conference1st IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2019
國家Taiwan
城市Hsinchu
期間19-03-1819-03-20

指紋

Liver
Tumors
Tissue
Cell death
Network architecture

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Hardware and Architecture
  • Electrical and Electronic Engineering

引用此文

Huang, W. C., Chung, P. C., Tsai, H. W., Chow, N. H., Juang, Y. Z., Tsai, H. H., ... Wang, C. H. (2019). Automatic HCC Detection Using Convolutional Network with Multi-Magnification Input Images. 於 Proceedings 2019 IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2019 (頁 194-198). [8771535] (Proceedings 2019 IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/AICAS.2019.8771535
Huang, Wei Che ; Chung, Pau Choo ; Tsai, Hung Wen ; Chow, Nan Haw ; Juang, Ying Zong ; Tsai, Hann Huei ; Lin, Shih Hsuan ; Wang, Cheng Hsiung. / Automatic HCC Detection Using Convolutional Network with Multi-Magnification Input Images. Proceedings 2019 IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2019. Institute of Electrical and Electronics Engineers Inc., 2019. 頁 194-198 (Proceedings 2019 IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2019).
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title = "Automatic HCC Detection Using Convolutional Network with Multi-Magnification Input Images",
abstract = "Liver cancer postoperative pathologic examination of stained tissues is an important step in identifying prognostic factors for follow-up care. Traditionally, liver cancer detection would be performed by pathologists with observing the entire biological tissue, resulting in heavy work loading and potential misjudgment. Accordingly, the studies of the automatic pathological examination have been popular for a long period of time. Most approaches of the existing cancer detection, however, only extract cell level information based on single-scale high-magnification patch. In liver tissues, common cell change phenomena such as apoptosis, necrosis, and steatosis are similar in tumor and benign. Hence, the detection may fail when the patch only covered the changed cells area that cannot provide enough neighboring cell structure information. To conquer this problem, the convolutional network architecture with multi-magnification input can provide not only the cell level information by referencing high-magnification patches, but also the cell structure information by referencing low-magnification patches. The detection algorithm consists of two main structures: 1) extraction of cell level and cell structure level feature maps from high-magnification and low-magnification images respectively by separate general convolutional networks, and 2) integration of multi-magnification features by fully connected network. In this paper, VGG16 and Inception V4 were applied as the based convolutional network for liver tumor detection task. The experimental results showed that VGG16 based multi-magnification input convolutional network achieved 91{\%} mIOU on HCC tumor detection task. In addition, with comparison between single-scale CNN (SSCN) and multi-scale CNN (MSCN) approaches, the MSCN demonstrated that the multi-scale patches could provide better performance on HCC classification task.",
author = "Huang, {Wei Che} and Chung, {Pau Choo} and Tsai, {Hung Wen} and Chow, {Nan Haw} and Juang, {Ying Zong} and Tsai, {Hann Huei} and Lin, {Shih Hsuan} and Wang, {Cheng Hsiung}",
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Huang, WC, Chung, PC, Tsai, HW, Chow, NH, Juang, YZ, Tsai, HH, Lin, SH & Wang, CH 2019, Automatic HCC Detection Using Convolutional Network with Multi-Magnification Input Images. 於 Proceedings 2019 IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2019., 8771535, Proceedings 2019 IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2019, Institute of Electrical and Electronics Engineers Inc., 頁 194-198, 1st IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2019, Hsinchu, Taiwan, 19-03-18. https://doi.org/10.1109/AICAS.2019.8771535

Automatic HCC Detection Using Convolutional Network with Multi-Magnification Input Images. / Huang, Wei Che; Chung, Pau Choo; Tsai, Hung Wen; Chow, Nan Haw; Juang, Ying Zong; Tsai, Hann Huei; Lin, Shih Hsuan; Wang, Cheng Hsiung.

Proceedings 2019 IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2019. Institute of Electrical and Electronics Engineers Inc., 2019. p. 194-198 8771535 (Proceedings 2019 IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2019).

研究成果: Conference contribution

TY - GEN

T1 - Automatic HCC Detection Using Convolutional Network with Multi-Magnification Input Images

AU - Huang, Wei Che

AU - Chung, Pau Choo

AU - Tsai, Hung Wen

AU - Chow, Nan Haw

AU - Juang, Ying Zong

AU - Tsai, Hann Huei

AU - Lin, Shih Hsuan

AU - Wang, Cheng Hsiung

PY - 2019/3

Y1 - 2019/3

N2 - Liver cancer postoperative pathologic examination of stained tissues is an important step in identifying prognostic factors for follow-up care. Traditionally, liver cancer detection would be performed by pathologists with observing the entire biological tissue, resulting in heavy work loading and potential misjudgment. Accordingly, the studies of the automatic pathological examination have been popular for a long period of time. Most approaches of the existing cancer detection, however, only extract cell level information based on single-scale high-magnification patch. In liver tissues, common cell change phenomena such as apoptosis, necrosis, and steatosis are similar in tumor and benign. Hence, the detection may fail when the patch only covered the changed cells area that cannot provide enough neighboring cell structure information. To conquer this problem, the convolutional network architecture with multi-magnification input can provide not only the cell level information by referencing high-magnification patches, but also the cell structure information by referencing low-magnification patches. The detection algorithm consists of two main structures: 1) extraction of cell level and cell structure level feature maps from high-magnification and low-magnification images respectively by separate general convolutional networks, and 2) integration of multi-magnification features by fully connected network. In this paper, VGG16 and Inception V4 were applied as the based convolutional network for liver tumor detection task. The experimental results showed that VGG16 based multi-magnification input convolutional network achieved 91% mIOU on HCC tumor detection task. In addition, with comparison between single-scale CNN (SSCN) and multi-scale CNN (MSCN) approaches, the MSCN demonstrated that the multi-scale patches could provide better performance on HCC classification task.

AB - Liver cancer postoperative pathologic examination of stained tissues is an important step in identifying prognostic factors for follow-up care. Traditionally, liver cancer detection would be performed by pathologists with observing the entire biological tissue, resulting in heavy work loading and potential misjudgment. Accordingly, the studies of the automatic pathological examination have been popular for a long period of time. Most approaches of the existing cancer detection, however, only extract cell level information based on single-scale high-magnification patch. In liver tissues, common cell change phenomena such as apoptosis, necrosis, and steatosis are similar in tumor and benign. Hence, the detection may fail when the patch only covered the changed cells area that cannot provide enough neighboring cell structure information. To conquer this problem, the convolutional network architecture with multi-magnification input can provide not only the cell level information by referencing high-magnification patches, but also the cell structure information by referencing low-magnification patches. The detection algorithm consists of two main structures: 1) extraction of cell level and cell structure level feature maps from high-magnification and low-magnification images respectively by separate general convolutional networks, and 2) integration of multi-magnification features by fully connected network. In this paper, VGG16 and Inception V4 were applied as the based convolutional network for liver tumor detection task. The experimental results showed that VGG16 based multi-magnification input convolutional network achieved 91% mIOU on HCC tumor detection task. In addition, with comparison between single-scale CNN (SSCN) and multi-scale CNN (MSCN) approaches, the MSCN demonstrated that the multi-scale patches could provide better performance on HCC classification task.

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Huang WC, Chung PC, Tsai HW, Chow NH, Juang YZ, Tsai HH 等. Automatic HCC Detection Using Convolutional Network with Multi-Magnification Input Images. 於 Proceedings 2019 IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2019. Institute of Electrical and Electronics Engineers Inc. 2019. p. 194-198. 8771535. (Proceedings 2019 IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2019). https://doi.org/10.1109/AICAS.2019.8771535