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.
All Science Journal Classification (ASJC) codes
- コンピュータ ビジョンおよびパターン認識