Full metadata record
DC Field | Value | Language |
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dc.contributor.author | Chikontwe, Philip | - |
dc.contributor.author | Nam, Soo Jeong | - |
dc.contributor.author | Go, Heounjeong | - |
dc.contributor.author | Kim, Meejeong | - |
dc.contributor.author | Sung, Hyun Jung | - |
dc.contributor.author | Park, Sang Hyun | - |
dc.date.accessioned | 2023-12-26T18:12:51Z | - |
dc.date.available | 2023-12-26T18:12:51Z | - |
dc.date.created | 2022-12-30 | - |
dc.date.issued | 2022-09-20 | - |
dc.identifier.isbn | 9783031164330 | - |
dc.identifier.issn | 0302-9743 | - |
dc.identifier.uri | http://hdl.handle.net/20.500.11750/46815 | - |
dc.description.abstract | Whole slide image (WSI) classification is a fundamental task for the diagnosis and treatment of diseases; but, curation of accurate labels is time-consuming and limits the application of fully-supervised methods. To address this, multiple instance learning (MIL) is a popular method that poses classification as a weakly supervised learning task with slide-level labels only. While current MIL methods apply variants of the attention mechanism to re-weight instance features with stronger models, scant attention is paid to the properties of the data distribution. In this work, we propose to re-calibrate the distribution of a WSI bag (instances) by using the statistics of the max-instance (critical) feature. We assume that in binary MIL, positive bags have larger feature magnitudes than negatives, thus we can enforce the model to maximize the discrepancy between bags with a metric feature loss that models positive bags as out-of-distribution. To achieve this, unlike existing MIL methods that use single-batch training modes, we propose balanced-batch sampling to effectively use the feature loss i.e., (+/−) bags simultaneously. Further, we employ a position encoding module (PEM) to model spatial/morphological information, and perform pooling by multi-head self-attention (PSMA) with a Transformer encoder. Experimental results on existing benchmark datasets show our approach is effective and improves over state-of-the-art MIL methods https://github.com/PhilipChicco/FRMIL. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG. | - |
dc.language | English | - |
dc.publisher | The Medical Image Computing and Computer Assisted Intervention Society | - |
dc.relation.ispartof | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | - |
dc.title | Feature Re-calibration Based Multiple Instance Learning for Whole Slide Image Classification | - |
dc.type | Conference Paper | - |
dc.identifier.doi | 10.1007/978-3-031-16434-7_41 | - |
dc.identifier.wosid | 000867288800041 | - |
dc.identifier.scopusid | 2-s2.0-85139035693 | - |
dc.identifier.bibliographicCitation | International Conference on Medical Image Computing and Computer Assisted Intervention, pp.420 - 430 | - |
dc.identifier.url | https://conferences.miccai.org/2022/files/downloads/MICCAI2022-Detailed-Program-new.pdf | - |
dc.citation.conferenceDate | 2022-09-18 | - |
dc.citation.conferencePlace | SI | - |
dc.citation.conferencePlace | Singapore | - |
dc.citation.endPage | 430 | - |
dc.citation.startPage | 420 | - |
dc.citation.title | International Conference on Medical Image Computing and Computer Assisted Intervention | - |
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