Adaptive weighting multi-field-of-view CNN for semantic segmentation in pathology

Hiroki Tokunaga, Yuki Teramoto, Akihiko Yoshizawa, Ryoma Bise

研究成果: Chapter in Book/Report/Conference proceedingConference contribution

27 被引用数 (Scopus)

抄録

Automated digital histopathology image segmentation is an important task to help pathologists diagnose tumors and cancer subtypes. For pathological diagnosis of cancer subtypes, pathologists usually change the magnification of whole-slide images (WSI) viewers. A key assumption is that the importance of the magnifications depends on the characteristics of the input image, such as cancer subtypes. In this paper, we propose a novel semantic segmentation method, called Adaptive-Weighting-Multi-Field-of-View-CNN (AWMF-CNN), that can adaptively use image features from images with different magnifications to segment multiple cancer subtype regions in the input image. The proposed method aggregates several expert CNNs for images of different magnifications by adaptively changing the weight of each expert depending on the input image. It leverages information in the images with different magnifications that might be useful for identifying the subtypes. It outperformed other state-of-the-art methods in experiments.

本文言語英語
ホスト出版物のタイトルProceedings - 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2019
出版社IEEE Computer Society
ページ12589-12598
ページ数10
ISBN(電子版)9781728132938
DOI
出版ステータス出版済み - 6 2019
イベント32nd IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2019 - Long Beach, 米国
継続期間: 6 16 20196 20 2019

出版物シリーズ

名前Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
2019-June
ISSN(印刷版)1063-6919

会議

会議32nd IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2019
国/地域米国
CityLong Beach
Period6/16/196/20/19

All Science Journal Classification (ASJC) codes

  • ソフトウェア
  • コンピュータ ビジョンおよびパターン認識

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引用スタイル