Multi-task Learning of Histology and Molecular Markers for Classifying Diffuse Glioma

Xiaofei Wang; Stephen Price; Chao Li

doi:10.1007/978-3-031-43990-2_52

Multi-task Learning of Histology and Molecular Markers for Classifying Diffuse Glioma

Xiaofei Wang, Stephen Price, Chao Li (Lead / Corresponding author)

Biomedical Engineering

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

Abstract

Most recently, the pathology diagnosis of cancer is shifting to integrating molecular makers with histology features. It is a urgent need for digital pathology methods to effectively integrate molecular markers with histology, which could lead to more accurate diagnosis in the real world scenarios. This paper presents a first attempt to jointly predict molecular markers and histology features and model their interactions for classifying diffuse glioma bases on whole slide images. Specifically, we propose a hierarchical multi-task multi-instance learning framework to jointly predict histology and molecular markers. Moreover, we propose a co-occurrence probability-based label correction graph network to model the co-occurrence of molecular markers. Lastly, we design an inter-omic interaction strategy with the dynamical confidence constraint loss to model the interactions of histology and molecular markers. Our experiments show that our method outperforms other state-of-the-art methods in classifying diffuse glioma, as well as related histology and molecular markers on a multi-institutional dataset.

Original language	English
Title of host publication	Medical Image Computing and Computer Assisted Intervention – MICCAI 2023
Subtitle of host publication	26th International Conference, Vancouver, BC, Canada, October 8–12, 2023, Proceedings, Part VII
Editors	Hayit Greenspan, Anant Madabhushi, Parvin Mousavi, Septimiu Salcudean, James Duncan, Tanveer Syeda-Mahmood, Russell Taylor
Publisher	Springer
Pages	551-561
Number of pages	11
Edition	1
ISBN (Electronic)	9783031439902
ISBN (Print)	9783031439896
DOIs	https://doi.org/10.1007/978-3-031-43990-2_52
Publication status	Published - 1 Oct 2023
Event	26th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2023 - Vancouver Convention Centre Canada, Vancouver, Canada Duration: 8 Oct 2023 → 12 Oct 2023 Conference number: 26 https://conferences.miccai.org/2023/en/ (Link to conference information)

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	14226
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	26th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2023
Abbreviated title	MICCAI
Country/Territory	Canada
City	Vancouver
Period	8/10/23 → 12/10/23
Internet address	https://conferences.miccai.org/2023/en/ (Link to conference information)

Keywords

Diffuse Glioma
Digital Pathology
Multi-task learning
Muti-label Classification

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

UN SDGs

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

Access to Document

10.1007/978-3-031-43990-2_52

Explainable Artificial Intelligence For Precision Neuro-Oncology - Joint with University of Cambridge - Fellowship
Li, C. (Investigator)
1/04/23 → 30/09/24
Project: Research

Cite this

Wang, X., Price, S., & Li, C. (2023). Multi-task Learning of Histology and Molecular Markers for Classifying Diffuse Glioma. In H. Greenspan, A. Madabhushi, P. Mousavi, S. Salcudean, J. Duncan, T. Syeda-Mahmood, & R. Taylor (Eds.), Medical Image Computing and Computer Assisted Intervention – MICCAI 2023: 26th International Conference, Vancouver, BC, Canada, October 8–12, 2023, Proceedings, Part VII (1 ed., pp. 551-561). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 14226 ). Springer . https://doi.org/10.1007/978-3-031-43990-2_52

Wang, Xiaofei ; Price, Stephen ; Li, Chao. / Multi-task Learning of Histology and Molecular Markers for Classifying Diffuse Glioma. Medical Image Computing and Computer Assisted Intervention – MICCAI 2023: 26th International Conference, Vancouver, BC, Canada, October 8–12, 2023, Proceedings, Part VII. editor / Hayit Greenspan ; Anant Madabhushi ; Parvin Mousavi ; Septimiu Salcudean ; James Duncan ; Tanveer Syeda-Mahmood ; Russell Taylor. 1. ed. Springer , 2023. pp. 551-561 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Multi-task Learning of Histology and Molecular Markers for Classifying Diffuse Glioma",

abstract = "Most recently, the pathology diagnosis of cancer is shifting to integrating molecular makers with histology features. It is a urgent need for digital pathology methods to effectively integrate molecular markers with histology, which could lead to more accurate diagnosis in the real world scenarios. This paper presents a first attempt to jointly predict molecular markers and histology features and model their interactions for classifying diffuse glioma bases on whole slide images. Specifically, we propose a hierarchical multi-task multi-instance learning framework to jointly predict histology and molecular markers. Moreover, we propose a co-occurrence probability-based label correction graph network to model the co-occurrence of molecular markers. Lastly, we design an inter-omic interaction strategy with the dynamical confidence constraint loss to model the interactions of histology and molecular markers. Our experiments show that our method outperforms other state-of-the-art methods in classifying diffuse glioma, as well as related histology and molecular markers on a multi-institutional dataset.",

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Wang, X, Price, S & Li, C 2023, Multi-task Learning of Histology and Molecular Markers for Classifying Diffuse Glioma. in H Greenspan, A Madabhushi, P Mousavi, S Salcudean, J Duncan, T Syeda-Mahmood & R Taylor (eds), Medical Image Computing and Computer Assisted Intervention – MICCAI 2023: 26th International Conference, Vancouver, BC, Canada, October 8–12, 2023, Proceedings, Part VII. 1 edn, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 14226 , Springer , pp. 551-561, 26th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2023, Vancouver, Canada, 8/10/23. https://doi.org/10.1007/978-3-031-43990-2_52

Multi-task Learning of Histology and Molecular Markers for Classifying Diffuse Glioma. / Wang, Xiaofei; Price, Stephen; Li, Chao (Lead / Corresponding author).
Medical Image Computing and Computer Assisted Intervention – MICCAI 2023: 26th International Conference, Vancouver, BC, Canada, October 8–12, 2023, Proceedings, Part VII. ed. / Hayit Greenspan; Anant Madabhushi; Parvin Mousavi; Septimiu Salcudean; James Duncan; Tanveer Syeda-Mahmood; Russell Taylor. 1. ed. Springer , 2023. p. 551-561 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 14226 ).

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

TY - GEN

T1 - Multi-task Learning of Histology and Molecular Markers for Classifying Diffuse Glioma

AU - Wang, Xiaofei

AU - Price, Stephen

AU - Li, Chao

N1 - Conference code: 26

PY - 2023/10/1

Y1 - 2023/10/1

N2 - Most recently, the pathology diagnosis of cancer is shifting to integrating molecular makers with histology features. It is a urgent need for digital pathology methods to effectively integrate molecular markers with histology, which could lead to more accurate diagnosis in the real world scenarios. This paper presents a first attempt to jointly predict molecular markers and histology features and model their interactions for classifying diffuse glioma bases on whole slide images. Specifically, we propose a hierarchical multi-task multi-instance learning framework to jointly predict histology and molecular markers. Moreover, we propose a co-occurrence probability-based label correction graph network to model the co-occurrence of molecular markers. Lastly, we design an inter-omic interaction strategy with the dynamical confidence constraint loss to model the interactions of histology and molecular markers. Our experiments show that our method outperforms other state-of-the-art methods in classifying diffuse glioma, as well as related histology and molecular markers on a multi-institutional dataset.

AB - Most recently, the pathology diagnosis of cancer is shifting to integrating molecular makers with histology features. It is a urgent need for digital pathology methods to effectively integrate molecular markers with histology, which could lead to more accurate diagnosis in the real world scenarios. This paper presents a first attempt to jointly predict molecular markers and histology features and model their interactions for classifying diffuse glioma bases on whole slide images. Specifically, we propose a hierarchical multi-task multi-instance learning framework to jointly predict histology and molecular markers. Moreover, we propose a co-occurrence probability-based label correction graph network to model the co-occurrence of molecular markers. Lastly, we design an inter-omic interaction strategy with the dynamical confidence constraint loss to model the interactions of histology and molecular markers. Our experiments show that our method outperforms other state-of-the-art methods in classifying diffuse glioma, as well as related histology and molecular markers on a multi-institutional dataset.

KW - Diffuse Glioma

KW - Digital Pathology

KW - Multi-task learning

KW - Muti-label Classification

U2 - 10.1007/978-3-031-43990-2_52

DO - 10.1007/978-3-031-43990-2_52

M3 - Conference contribution

AN - SCOPUS:85174747637

SN - 9783031439896

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

SP - 551

EP - 561

BT - Medical Image Computing and Computer Assisted Intervention – MICCAI 2023

A2 - Greenspan, Hayit

A2 - Madabhushi, Anant

A2 - Mousavi, Parvin

A2 - Salcudean, Septimiu

A2 - Duncan, James

A2 - Syeda-Mahmood, Tanveer

A2 - Taylor, Russell

PB - Springer

T2 - 26th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2023

Y2 - 8 October 2023 through 12 October 2023

ER -

Wang X, Price S, Li C. Multi-task Learning of Histology and Molecular Markers for Classifying Diffuse Glioma. In Greenspan H, Madabhushi A, Mousavi P, Salcudean S, Duncan J, Syeda-Mahmood T, Taylor R, editors, Medical Image Computing and Computer Assisted Intervention – MICCAI 2023: 26th International Conference, Vancouver, BC, Canada, October 8–12, 2023, Proceedings, Part VII. 1 ed. Springer . 2023. p. 551-561. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-031-43990-2_52