Whole Slide Multiple Instance Learning for Predicting Axillary Lymph Node Metastasis

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

doi:10.1007/978-3-031-44992-5_2

Whole Slide Multiple Instance Learning for Predicting Axillary Lymph Node Metastasis

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

Assistant Professorship of Computational Pathology

Friedrich-Alexander Universitat Erlangen-Nurnberg (FAU)
Imperial College London

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Scopus citations

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 language	English
Title of host publication	Data Engineering in Medical Imaging - 1st MICCAI Workshop, DEMI 2023, Held in Conjunction with MICCAI 2023, Proceedings
Editors	Binod Bhattarai, Sharib Ali, Anita Rau, Anh Nguyen, Ana Namburete, Razvan Caramalau, Danail Stoyanov
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	11-20
Number of pages	10
ISBN (Print)	9783031449918
DOIs	https://doi.org/10.1007/978-3-031-44992-5_2
State	Published - 2023
Event	1st MICCAI Workshop on Data Engineering in Medical Imaging, DEMI 2023 - Vancouver, Canada Duration: 8 Oct 2023 → 8 Oct 2023

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	14314 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	1st MICCAI Workshop on Data Engineering in Medical Imaging, DEMI 2023
Country/Territory	Canada
City	Vancouver
Period	8/10/23 → 8/10/23

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Access to Document

10.1007/978-3-031-44992-5_2

Cite this

Shkëmbi, G., Müller, J. P., Li, Z., Breininger, K., Schüffler, P., & Kainz, B. (2023). Whole Slide Multiple Instance Learning for Predicting Axillary Lymph Node Metastasis. In B. Bhattarai, S. Ali, A. Rau, A. Nguyen, A. Namburete, R. Caramalau, & D. Stoyanov (Eds.), Data Engineering in Medical Imaging - 1st MICCAI Workshop, DEMI 2023, Held in Conjunction with MICCAI 2023, Proceedings (pp. 11-20). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 14314 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-44992-5_2

Shkëmbi, Glejdis ; Müller, Johanna P. ; Li, Zhe et al. / Whole Slide Multiple Instance Learning for Predicting Axillary Lymph Node Metastasis. Data Engineering in Medical Imaging - 1st MICCAI Workshop, DEMI 2023, Held in Conjunction with MICCAI 2023, Proceedings. editor / Binod Bhattarai ; Sharib Ali ; Anita Rau ; Anh Nguyen ; Ana Namburete ; Razvan Caramalau ; Danail Stoyanov. Springer Science and Business Media Deutschland GmbH, 2023. pp. 11-20 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{7ac22626618544bdb9fc614279b4112b,

title = "Whole Slide Multiple Instance Learning for Predicting Axillary Lymph Node Metastasis",

abstract = "Breast cancer is a major concern for women{\textquoteright}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.",

author = "Glejdis Shk{\"e}mbi and M{\"u}ller, \{Johanna P.\} and Zhe Li and Katharina Breininger and Peter Sch{\"u}ffler and Bernhard Kainz",

note = "Publisher Copyright: {\textcopyright} 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.; 1st MICCAI Workshop on Data Engineering in Medical Imaging, DEMI 2023 ; Conference date: 08-10-2023 Through 08-10-2023",

year = "2023",

doi = "10.1007/978-3-031-44992-5\_2",

language = "English",

isbn = "9783031449918",

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 = "11--20",

editor = "Binod Bhattarai and Sharib Ali and Anita Rau and Anh Nguyen and Ana Namburete and Razvan Caramalau and Danail Stoyanov",

booktitle = "Data Engineering in Medical Imaging - 1st MICCAI Workshop, DEMI 2023, Held in Conjunction with MICCAI 2023, Proceedings",

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Shkëmbi, G, Müller, JP, Li, Z, Breininger, K, Schüffler, P & Kainz, B 2023, Whole Slide Multiple Instance Learning for Predicting Axillary Lymph Node Metastasis. in B Bhattarai, S Ali, A Rau, A Nguyen, A Namburete, R Caramalau & D Stoyanov (eds), Data Engineering in Medical Imaging - 1st MICCAI Workshop, DEMI 2023, Held in Conjunction with MICCAI 2023, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 14314 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 11-20, 1st MICCAI Workshop on Data Engineering in Medical Imaging, DEMI 2023, Vancouver, Canada, 8/10/23. https://doi.org/10.1007/978-3-031-44992-5_2

Whole Slide Multiple Instance Learning for Predicting Axillary Lymph Node Metastasis. / Shkëmbi, Glejdis; Müller, Johanna P.; Li, Zhe et al.
Data Engineering in Medical Imaging - 1st MICCAI Workshop, DEMI 2023, Held in Conjunction with MICCAI 2023, Proceedings. ed. / Binod Bhattarai; Sharib Ali; Anita Rau; Anh Nguyen; Ana Namburete; Razvan Caramalau; Danail Stoyanov. Springer Science and Business Media Deutschland GmbH, 2023. p. 11-20 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 14314 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Whole Slide Multiple Instance Learning for Predicting Axillary Lymph Node Metastasis

AU - Shkëmbi, Glejdis

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AU - Li, Zhe

AU - Breininger, Katharina

AU - Schüffler, Peter

AU - Kainz, Bernhard

PY - 2023

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N2 - 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.

AB - 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.

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DO - 10.1007/978-3-031-44992-5_2

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AN - SCOPUS:85174639121

SN - 9783031449918

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 11

EP - 20

BT - Data Engineering in Medical Imaging - 1st MICCAI Workshop, DEMI 2023, Held in Conjunction with MICCAI 2023, Proceedings

A2 - Bhattarai, Binod

A2 - Ali, Sharib

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A2 - Namburete, Ana

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A2 - Stoyanov, Danail

PB - Springer Science and Business Media Deutschland GmbH

T2 - 1st MICCAI Workshop on Data Engineering in Medical Imaging, DEMI 2023

Y2 - 8 October 2023 through 8 October 2023

ER -

Shkëmbi G, Müller JP, Li Z, Breininger K, Schüffler P, Kainz B. Whole Slide Multiple Instance Learning for Predicting Axillary Lymph Node Metastasis. In Bhattarai B, Ali S, Rau A, Nguyen A, Namburete A, Caramalau R, Stoyanov D, editors, Data Engineering in Medical Imaging - 1st MICCAI Workshop, DEMI 2023, Held in Conjunction with MICCAI 2023, Proceedings. Springer Science and Business Media Deutschland GmbH. 2023. p. 11-20. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-031-44992-5_2