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

Professur für Computational Pathology

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

Publikation: Beitrag in Buch/Bericht/Konferenzband › Konferenzbeitrag › Begutachtung

2 Zitate (Scopus)

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.

Originalsprache	Englisch
Titel	Data Engineering in Medical Imaging - 1st MICCAI Workshop, DEMI 2023, Held in Conjunction with MICCAI 2023, Proceedings
Redakteure/-innen	Binod Bhattarai, Sharib Ali, Anita Rau, Anh Nguyen, Ana Namburete, Razvan Caramalau, Danail Stoyanov
Herausgeber (Verlag)	Springer Science and Business Media Deutschland GmbH
Seiten	11-20
Seitenumfang	10
ISBN (Print)	9783031449918
DOIs	https://doi.org/10.1007/978-3-031-44992-5_2
Publikationsstatus	Veröffentlicht - 2023
Veranstaltung	1st MICCAI Workshop on Data Engineering in Medical Imaging, DEMI 2023 - Vancouver, Kanada Dauer: 8 Okt. 2023 → 8 Okt. 2023

Publikationsreihe

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

Konferenz

Konferenz	1st MICCAI Workshop on Data Engineering in Medical Imaging, DEMI 2023
Land/Gebiet	Kanada
Ort	Vancouver
Zeitraum	8/10/23 → 8/10/23

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Zugriff auf Dokument

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

Andere Dateien und Links

Verknüpfung zur Publikation in Scopus

Dieses zitieren

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 (Hrsg.), Data Engineering in Medical Imaging - 1st MICCAI Workshop, DEMI 2023, Held in Conjunction with MICCAI 2023, Proceedings (S. 11-20). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Band 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. Hrsg. / Binod Bhattarai ; Sharib Ali ; Anita Rau ; Anh Nguyen ; Ana Namburete ; Razvan Caramalau ; Danail Stoyanov. Springer Science and Business Media Deutschland GmbH, 2023. S. 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 (Hrsg.), 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), Bd. 14314 LNCS, Springer Science and Business Media Deutschland GmbH, S. 11-20, 1st MICCAI Workshop on Data Engineering in Medical Imaging, DEMI 2023, Vancouver, Kanada, 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. Hrsg. / Binod Bhattarai; Sharib Ali; Anita Rau; Anh Nguyen; Ana Namburete; Razvan Caramalau; Danail Stoyanov. Springer Science and Business Media Deutschland GmbH, 2023. S. 11-20 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Band 14314 LNCS).

Publikation: Beitrag in Buch/Bericht/Konferenzband › Konferenzbeitrag › Begutachtung

TY - GEN

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

AU - Shkëmbi, Glejdis

AU - Müller, Johanna P.

AU - Li, Zhe

AU - Breininger, Katharina

AU - Schüffler, Peter

AU - Kainz, Bernhard

PY - 2023

Y1 - 2023

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.

UR - https://www.scopus.com/pages/publications/85174639121

U2 - 10.1007/978-3-031-44992-5_2

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

M3 - Conference contribution

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

A2 - Rau, Anita

A2 - Nguyen, Anh

A2 - Namburete, Ana

A2 - Caramalau, Razvan

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, Hrsg., 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. S. 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