Top-rank convolutional neural network and its application to medical image-based diagnosis

Yan Zheng, Yuchen Zheng, Daiki Suehiro, Seiichi Uchida

研究成果: Contribution to journalArticle査読

抄録

Top-rank learning identifies a real-valued ranking function that will provide more absolute top samples. These are highly reliable positive samples that are ranked higher than the highest-ranked negative samples. Therefore, top-rank learning is useful for tasks that require reliable decisions. Additionally, it inherits the merits of the ranking functions, such as robustness to the unbalanced condition. However, conventional top-rank learning tasks are formulated as linear or kernel-based problems and are thus limited in coping with complicated tasks. In this study, we propose a Top-rank convolutional neural network (TopRank CNN) to realize top-rank learning with representation learning for complicated tasks. Given that the original objective function of top-rank learning suffers from overfitting, we employ the p-norm relaxation of the original loss function in the proposed method. We prove the usefulness of TopRank CNN experimentally with medical diagnosis tasks that require reliable decisions and robustness to the unbalanced condition.

本文言語英語
論文番号108138
ジャーナルPattern Recognition
120
DOI
出版ステータス出版済み - 12 2021

All Science Journal Classification (ASJC) codes

  • ソフトウェア
  • 信号処理
  • コンピュータ ビジョンおよびパターン認識
  • 人工知能

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