Whole Slide Multiple Instance Learning for Predicting Axillary Lymph Node Metastasis
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Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 9 of its 16 references that are in the map. First posted on arXiv 6 Oct 2023.
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References · 9
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- 2015Rethinking the Inception Architecture for Computer Visioncited by 5,451
- 2019RandAugment: Practical automated data augmentation with a reduced search spacecited by 2,261
- 2018Attention-based Deep Multiple Instance Learningcited by 1,077
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- 2021TrivialAugment: Tuning-free Yet State-of-the-Art Data Augmentationcited by 225
- 2021Predicting Axillary Lymph Node Metastasis in Early Breast Cancer Using Deep Learning on Primary Tumor Biopsy Slidescited by 43
- 2020Quantifying Explainers of Graph Neural Networks in Computational Pathologycited by 35
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