Whole Slide Multiple Instance Learning for Predicting Axillary Lymph Node Metastasis

Glejdis Shkëmbi, Johanna P. Müller, Zhe Li, Katharina Breininger, Peter Schüffler, Bernhard Kainz

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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.

Original languageEnglish
Title of host publicationData Engineering in Medical Imaging - 1st MICCAI Workshop, DEMI 2023, Held in Conjunction with MICCAI 2023, Proceedings
EditorsBinod Bhattarai, Sharib Ali, Anita Rau, Anh Nguyen, Ana Namburete, Razvan Caramalau, Danail Stoyanov
PublisherSpringer Science and Business Media Deutschland GmbH
Pages11-20
Number of pages10
ISBN (Print)9783031449918
DOIs
StatePublished - 2023
Event1st MICCAI Workshop on Data Engineering in Medical Imaging, DEMI 2023 - Vancouver, Canada
Duration: 8 Oct 20238 Oct 2023

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume14314 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference1st MICCAI Workshop on Data Engineering in Medical Imaging, DEMI 2023
Country/TerritoryCanada
CityVancouver
Period8/10/238/10/23

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