Whole slide multiple instance learning for predicting axillary lymph node metastasis
File(s)
Accepted version
Author(s)
Type
Chapter
Abstract
Breast cancer is a major concern for women’s health globally, with axillary lymph node (ALN) metastasis identification being critical for prognosis evaluation and treatment guidance. This paper presents a deep learning (DL) classification pipeline for quantifying clinical information from digital core-needle biopsy (CNB) images, with one step less than existing methods. A publicly available dataset of 1058 patients was used to evaluate the performance of different baseline state-of-the-art (SOTA) DL models in classifying ALN metastatic status based on CNB images. An extensive ablation study of various data augmentation techniques was also conducted. Finally, the manual tumor segmentation and annotation step performed by the pathologists was assessed. Our proposed training scheme outperformed SOTA by 3.73%. Source code is available here.
Editor(s)
Bhattarai, B
Ali, S
Rau, A
Nguyen, A
Namburete, A
Caramalau, R
Stoyanov, D
Date Issued
2023-10-01
Citation
Data Engineering in Medical Imaging, 2023, 14314, pp.11-20
ISBN
978-3-031-44991-8
Publisher
Springer Nature Switzerland AG
Start Page
11
End Page
20
Journal / Book Title
Data Engineering in Medical Imaging
Lecture Notes in Computer Science
Volume
14314
Copyright Statement
© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG. This version of the article has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: http://dx.doi.org/10.1007/978-3-031-44992-5_2
Subjects
Computer Science
Computer Science, Artificial Intelligence
Computer Science, Software Engineering
Computer Science, Theory & Methods
Engineering
Engineering, Biomedical
Science & Technology
Technology
Publication Status
Published
Date Publish Online
2023-10-01