Semi-supervised learning with structured knowledge for body hair detection in photoacoustic image

Ryo Kikkawa, Hiroyuki Sekiguchi, Itaru Tsuge, Susumu Saito, Ryoma Bise

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

3 被引用数 (Scopus)

抄録

Photoacoustic (PA) imaging is a promising new imaging technology for non-invasively visualizing blood vessels inside biological tissues. In addition to blood vessels, body hairs are also visualized in PA imaging, and the body hair signals degrade the visibility of blood vessels. For learning a body hair classifier, the amount of real training and test data is limited, because PA imaging is a new modality. To address this problem, we propose a novel semi-supervised learning (SSL) method for extracting body hairs. The method effectively learns the discriminative model from small labeled training data and small unlabeled test data by introducing prior knowledge, of the orientation similarity among adjacent body hairs, into SSL. Experimental results using real PA data demonstrate that the proposed approach is effective for extracting body hairs as compared with several baseline methods.

本文言語英語
ホスト出版物のタイトルISBI 2019 - 2019 IEEE International Symposium on Biomedical Imaging
出版社IEEE Computer Society
ページ1411-1415
ページ数5
ISBN(電子版)9781538636411
DOI
出版ステータス出版済み - 4 2019
イベント16th IEEE International Symposium on Biomedical Imaging, ISBI 2019 - Venice, イタリア
継続期間: 4 8 20194 11 2019

出版物シリーズ

名前Proceedings - International Symposium on Biomedical Imaging
2019-April
ISSN(印刷版)1945-7928
ISSN(電子版)1945-8452

会議

会議16th IEEE International Symposium on Biomedical Imaging, ISBI 2019
Countryイタリア
CityVenice
Period4/8/194/11/19

All Science Journal Classification (ASJC) codes

  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging

フィンガープリント 「Semi-supervised learning with structured knowledge for body hair detection in photoacoustic image」の研究トピックを掘り下げます。これらがまとまってユニークなフィンガープリントを構成します。

引用スタイル