Calculating tumor proportional score of HNSCC patients with deep learning object detection

Po Hang Tseng; Jin Tang Lin; Xin Yu Liao; Sheng Lin Lee; Mei Chun Lin; Yen Lin Huang; Pei Jen Lou; Chen Yuan Dong

doi:10.1117/12.2615639

Calculating tumor proportional score of HNSCC patients with deep learning object detection

Po Hang Tseng, Jin Tang Lin, Xin Yu Liao, Sheng Lin Lee, Mei Chun Lin, Yen Lin Huang, Pei Jen Lou, Chen Yuan Dong^*

^*Corresponding author for this work

National Taiwan University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

This work illustrates how tumor proportional score is estimated using object detection method YOLO and compared with a pathologist's calculation. Results show deep learning can achieve good results and be used on clinical applications.

Original language	English
Title of host publication	Emerging Technologies for Cell and Tissue Characterization
Editors	Arjen Amelink, Seemantini K. Nadkarni, Giuliano Scarcelli
Publisher	SPIE
ISBN (Electronic)	9781510647084
DOIs	https://doi.org/10.1117/12.2615639
State	Published - 2021
Externally published	Yes
Event	Emerging Technologies for Cell and Tissue Characterization 2021 - Virtual, Online, Germany Duration: 20 06 2021 → 24 06 2021

Publication series

Name	Progress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume	11921
ISSN (Print)	1605-7422

Conference

Conference	Emerging Technologies for Cell and Tissue Characterization 2021
Country/Territory	Germany
City	Virtual, Online
Period	20/06/21 → 24/06/21

Bibliographical note

Publisher Copyright:
© 2021 OSA-SPIE.

Access to Document

10.1117/12.2615639

Cite this

Tseng, P. H., Lin, J. T., Liao, X. Y., Lee, S. L., Lin, M. C., Huang, Y. L., Lou, P. J., & Dong, C. Y. (2021). Calculating tumor proportional score of HNSCC patients with deep learning object detection. In A. Amelink, S. K. Nadkarni, & G. Scarcelli (Eds.), Emerging Technologies for Cell and Tissue Characterization Article 119210L (Progress in Biomedical Optics and Imaging - Proceedings of SPIE; Vol. 11921). SPIE. https://doi.org/10.1117/12.2615639

@inproceedings{69d06e1d9538424f85665e822b29a732,

title = "Calculating tumor proportional score of HNSCC patients with deep learning object detection",

abstract = "This work illustrates how tumor proportional score is estimated using object detection method YOLO and compared with a pathologist's calculation. Results show deep learning can achieve good results and be used on clinical applications.",

author = "Tseng, \{Po Hang\} and Lin, \{Jin Tang\} and Liao, \{Xin Yu\} and Lee, \{Sheng Lin\} and Lin, \{Mei Chun\} and Huang, \{Yen Lin\} and Lou, \{Pei Jen\} and Dong, \{Chen Yuan\}",

note = "Publisher Copyright: {\textcopyright} 2021 OSA-SPIE.; Emerging Technologies for Cell and Tissue Characterization 2021 ; Conference date: 20-06-2021 Through 24-06-2021",

year = "2021",

doi = "10.1117/12.2615639",

language = "英语",

series = "Progress in Biomedical Optics and Imaging - Proceedings of SPIE",

publisher = "SPIE",

editor = "Arjen Amelink and Nadkarni, \{Seemantini K.\} and Giuliano Scarcelli",

booktitle = "Emerging Technologies for Cell and Tissue Characterization",

address = "美国",

}

Tseng, PH, Lin, JT, Liao, XY, Lee, SL, Lin, MC, Huang, YL, Lou, PJ & Dong, CY 2021, Calculating tumor proportional score of HNSCC patients with deep learning object detection. in A Amelink, SK Nadkarni & G Scarcelli (eds), Emerging Technologies for Cell and Tissue Characterization., 119210L, Progress in Biomedical Optics and Imaging - Proceedings of SPIE, vol. 11921, SPIE, Emerging Technologies for Cell and Tissue Characterization 2021, Virtual, Online, Germany, 20/06/21. https://doi.org/10.1117/12.2615639

Calculating tumor proportional score of HNSCC patients with deep learning object detection. / Tseng, Po Hang; Lin, Jin Tang; Liao, Xin Yu et al.
Emerging Technologies for Cell and Tissue Characterization. ed. / Arjen Amelink; Seemantini K. Nadkarni; Giuliano Scarcelli. SPIE, 2021. 119210L (Progress in Biomedical Optics and Imaging - Proceedings of SPIE; Vol. 11921).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Calculating tumor proportional score of HNSCC patients with deep learning object detection

AU - Tseng, Po Hang

AU - Lin, Jin Tang

AU - Liao, Xin Yu

AU - Lee, Sheng Lin

AU - Lin, Mei Chun

AU - Huang, Yen Lin

AU - Lou, Pei Jen

AU - Dong, Chen Yuan

PY - 2021

Y1 - 2021

N2 - This work illustrates how tumor proportional score is estimated using object detection method YOLO and compared with a pathologist's calculation. Results show deep learning can achieve good results and be used on clinical applications.

AB - This work illustrates how tumor proportional score is estimated using object detection method YOLO and compared with a pathologist's calculation. Results show deep learning can achieve good results and be used on clinical applications.

UR - https://www.scopus.com/pages/publications/85122929296

U2 - 10.1117/12.2615639

DO - 10.1117/12.2615639

M3 - 会议稿件

AN - SCOPUS:85122929296

T3 - Progress in Biomedical Optics and Imaging - Proceedings of SPIE

BT - Emerging Technologies for Cell and Tissue Characterization

A2 - Amelink, Arjen

A2 - Nadkarni, Seemantini K.

A2 - Scarcelli, Giuliano

PB - SPIE

T2 - Emerging Technologies for Cell and Tissue Characterization 2021

Y2 - 20 June 2021 through 24 June 2021

ER -

Tseng PH, Lin JT, Liao XY, Lee SL, Lin MC, Huang YL et al. Calculating tumor proportional score of HNSCC patients with deep learning object detection. In Amelink A, Nadkarni SK, Scarcelli G, editors, Emerging Technologies for Cell and Tissue Characterization. SPIE. 2021. 119210L. (Progress in Biomedical Optics and Imaging - Proceedings of SPIE). doi: 10.1117/12.2615639

Calculating tumor proportional score of HNSCC patients with deep learning object detection

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