@inproceedings{ee6bdd90a15041bf8e5838309c5ba0c5,
title = "MLCN: Metric Learning Constrained Network for Whole Slide Image Classification with Bilinear Gated Attention Mechanism",
abstract = "Whole Slide Image (WSI) classification is an important part of pathological diagnosis. Although previous approaches (such as DSMIL and CLAM) have achieved good results, the classification performance is still unsatisfactory because the learned features of WSI lack discrimination and the correlation among sub-characteristics of tumor images are ignored. In this paper, we proposed a Metric Learning Constraint Network (referred to as MLCN). Particularly, MLCN benefits from two aspects: 1) It enhances the discriminative power of features by enlarging inter-class distance and narrowing intra-class distance in both slide-level and patch-level. 2) It learns a more powerful feature aggregator by proposing the bilinear gated attention mechanism to capture relations among sub-characteristics of tumor issues. Experiments on CAMELYON16 and TCGA Kidney datasets validate the effectiveness of our approach, and we achieved state-of-the-art performance compared to other popular methods. The codes will be available soon.",
keywords = "Attention mechanism, Deep learning, Metric learning, Pathological image, Whole Slide Image classification",
author = "Baorong Shi and Xinyu Liu and Fa Zhang",
note = "Publisher Copyright: {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.; 1st International Workshop on Computational Mathematics Modeling in Cancer Analysis, CMMCA 2022, held in conjunction with the 25th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2022 ; Conference date: 18-09-2022 Through 18-09-2022",
year = "2022",
doi = "10.1007/978-3-031-17266-3_4",
language = "English",
isbn = "9783031172656",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "35--46",
editor = "Wenjian Qin and Nazar Zaki and Fa Zhang and Jia Wu and Fan Yang",
booktitle = "Computational Mathematics Modeling in Cancer Analysis - 1st International Workshop, CMMCA 2022, Held in Conjunction with MICCAI 2022, Proceedings",
address = "Germany",
}