Dysplasia Grading of Colorectal Polyps Through Convolutional Neural Network Analysis of Whole Slide Images

Daniele Perlo; Enzo Tartaglione; Luca Bertero; Paola Cassoni; Marco Grangetto

doi:10.1007/978-981-16-3880-0_34

Dysplasia Grading of Colorectal Polyps Through Convolutional Neural Network Analysis of Whole Slide Images

Daniele Perlo
, Enzo Tartaglione
, Luca Bertero
, Paola Cassoni
, Marco Grangetto

University of Turin
University of Torino Medical School

Résultats de recherche: Le chapitre dans un livre, un rapport, une anthologie ou une collection › Contribution à une conférence › Revue par des pairs

6 Citations (Scopus)

Résumé

Colorectal cancer is a leading cause of cancer death for both men and women. For this reason, histo-pathological characterization of colorectal polyps is the major instrument for the pathologist in order to infer the actual risk for cancer and to guide further follow-up. Colorectal polyps diagnosis includes the evaluation of the polyp type, and more importantly, the grade of dysplasia. This latter evaluation represents a critical step for the clinical follow-up. The proposed deep learning-based classification pipeline is based on state-of-the-art convolutional neural network, trained using proper countermeasures to tackle WSI high resolution and very imbalanced dataset. The experimental results show that one can successfully classify adenomas dysplasia grade with 70% accuracy, which is in line with the pathologists’ concordance.

langue originale	Anglais
titre	Proceedings of 2021 International Conference on Medical Imaging and Computer-Aided Diagnosis, MICAD 2021 - Medical Imaging and Computer-Aided Diagnosis
rédacteurs en chef	Ruidan Su, Yu-Dong Zhang, Han Liu
Editeur	Springer Science and Business Media Deutschland GmbH
Pages	325-334
Nombre de pages	10
ISBN (imprimé)	9789811638794
Les DOIs	https://doi.org/10.1007/978-981-16-3880-0_34
état	Publié - 1 janv. 2022
Modification externe	Oui
Evénement	International Conference on Medical Imaging and Computer-Aided Diagnosis, MICAD 2021 - Virtual, Online Durée: 25 mars 2021 → 26 mars 2021

Série de publications

Nom	Lecture Notes in Electrical Engineering
Volume	784 LNEE
ISSN (imprimé)	1876-1100
ISSN (Electronique)	1876-1119

Une conférence

Une conférence	International Conference on Medical Imaging and Computer-Aided Diagnosis, MICAD 2021
La ville	Virtual, Online
période	25/03/21 → 26/03/21

SDG des Nations Unies

Ce résultat contribue à ou aux Objectifs de développement durable suivants

SDG 3 Bonne santé et bien-être

Accès au document

10.1007/978-981-16-3880-0_34

Contient cette citation

Perlo, D., Tartaglione, E., Bertero, L., Cassoni, P., & Grangetto, M. (2022). Dysplasia Grading of Colorectal Polyps Through Convolutional Neural Network Analysis of Whole Slide Images. Dans R. Su, Y.-D. Zhang, & H. Liu (eds.), Proceedings of 2021 International Conference on Medical Imaging and Computer-Aided Diagnosis, MICAD 2021 - Medical Imaging and Computer-Aided Diagnosis (p. 325-334). (Lecture Notes in Electrical Engineering; Vol 784 LNEE). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-981-16-3880-0_34

Perlo, Daniele ; Tartaglione, Enzo ; Bertero, Luca et al. / Dysplasia Grading of Colorectal Polyps Through Convolutional Neural Network Analysis of Whole Slide Images. Proceedings of 2021 International Conference on Medical Imaging and Computer-Aided Diagnosis, MICAD 2021 - Medical Imaging and Computer-Aided Diagnosis. Editeur / Ruidan Su ; Yu-Dong Zhang ; Han Liu. Springer Science and Business Media Deutschland GmbH, 2022. p. 325-334 (Lecture Notes in Electrical Engineering).

@inproceedings{99e8bf59d45547789e47416707537d0f,

title = "Dysplasia Grading of Colorectal Polyps Through Convolutional Neural Network Analysis of Whole Slide Images",

abstract = "Colorectal cancer is a leading cause of cancer death for both men and women. For this reason, histo-pathological characterization of colorectal polyps is the major instrument for the pathologist in order to infer the actual risk for cancer and to guide further follow-up. Colorectal polyps diagnosis includes the evaluation of the polyp type, and more importantly, the grade of dysplasia. This latter evaluation represents a critical step for the clinical follow-up. The proposed deep learning-based classification pipeline is based on state-of-the-art convolutional neural network, trained using proper countermeasures to tackle WSI high resolution and very imbalanced dataset. The experimental results show that one can successfully classify adenomas dysplasia grade with 70\% accuracy, which is in line with the pathologists{\textquoteright} concordance.",

keywords = "Colorectal adenomas, Colorectal polyps, Deep learning, Digital pathology, Multi resolution",

author = "Daniele Perlo and Enzo Tartaglione and Luca Bertero and Paola Cassoni and Marco Grangetto",

note = "Publisher Copyright: {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.; International Conference on Medical Imaging and Computer-Aided Diagnosis, MICAD 2021 ; Conference date: 25-03-2021 Through 26-03-2021",

year = "2022",

month = jan,

day = "1",

doi = "10.1007/978-981-16-3880-0\_34",

language = "English",

isbn = "9789811638794",

series = "Lecture Notes in Electrical Engineering",

publisher = "Springer Science and Business Media Deutschland GmbH",

pages = "325--334",

editor = "Ruidan Su and Yu-Dong Zhang and Han Liu",

booktitle = "Proceedings of 2021 International Conference on Medical Imaging and Computer-Aided Diagnosis, MICAD 2021 - Medical Imaging and Computer-Aided Diagnosis",

}

Perlo, D, Tartaglione, E, Bertero, L, Cassoni, P & Grangetto, M 2022, Dysplasia Grading of Colorectal Polyps Through Convolutional Neural Network Analysis of Whole Slide Images. Dans R Su, Y-D Zhang & H Liu (eds), Proceedings of 2021 International Conference on Medical Imaging and Computer-Aided Diagnosis, MICAD 2021 - Medical Imaging and Computer-Aided Diagnosis. Lecture Notes in Electrical Engineering, VOL. 784 LNEE, Springer Science and Business Media Deutschland GmbH, p. 325-334, International Conference on Medical Imaging and Computer-Aided Diagnosis, MICAD 2021, Virtual, Online, 25/03/21. https://doi.org/10.1007/978-981-16-3880-0_34

Dysplasia Grading of Colorectal Polyps Through Convolutional Neural Network Analysis of Whole Slide Images. / Perlo, Daniele; Tartaglione, Enzo; Bertero, Luca et al.
Proceedings of 2021 International Conference on Medical Imaging and Computer-Aided Diagnosis, MICAD 2021 - Medical Imaging and Computer-Aided Diagnosis. Ed. / Ruidan Su; Yu-Dong Zhang; Han Liu. Springer Science and Business Media Deutschland GmbH, 2022. p. 325-334 (Lecture Notes in Electrical Engineering; Vol 784 LNEE).

Résultats de recherche: Le chapitre dans un livre, un rapport, une anthologie ou une collection › Contribution à une conférence › Revue par des pairs

TY - GEN

T1 - Dysplasia Grading of Colorectal Polyps Through Convolutional Neural Network Analysis of Whole Slide Images

AU - Perlo, Daniele

AU - Tartaglione, Enzo

AU - Bertero, Luca

AU - Cassoni, Paola

AU - Grangetto, Marco

PY - 2022/1/1

Y1 - 2022/1/1

N2 - Colorectal cancer is a leading cause of cancer death for both men and women. For this reason, histo-pathological characterization of colorectal polyps is the major instrument for the pathologist in order to infer the actual risk for cancer and to guide further follow-up. Colorectal polyps diagnosis includes the evaluation of the polyp type, and more importantly, the grade of dysplasia. This latter evaluation represents a critical step for the clinical follow-up. The proposed deep learning-based classification pipeline is based on state-of-the-art convolutional neural network, trained using proper countermeasures to tackle WSI high resolution and very imbalanced dataset. The experimental results show that one can successfully classify adenomas dysplasia grade with 70% accuracy, which is in line with the pathologists’ concordance.

AB - Colorectal cancer is a leading cause of cancer death for both men and women. For this reason, histo-pathological characterization of colorectal polyps is the major instrument for the pathologist in order to infer the actual risk for cancer and to guide further follow-up. Colorectal polyps diagnosis includes the evaluation of the polyp type, and more importantly, the grade of dysplasia. This latter evaluation represents a critical step for the clinical follow-up. The proposed deep learning-based classification pipeline is based on state-of-the-art convolutional neural network, trained using proper countermeasures to tackle WSI high resolution and very imbalanced dataset. The experimental results show that one can successfully classify adenomas dysplasia grade with 70% accuracy, which is in line with the pathologists’ concordance.

KW - Colorectal adenomas

KW - Colorectal polyps

KW - Deep learning

KW - Digital pathology

KW - Multi resolution

U2 - 10.1007/978-981-16-3880-0_34

DO - 10.1007/978-981-16-3880-0_34

M3 - Conference contribution

AN - SCOPUS:85115167864

SN - 9789811638794

T3 - Lecture Notes in Electrical Engineering

SP - 325

EP - 334

BT - Proceedings of 2021 International Conference on Medical Imaging and Computer-Aided Diagnosis, MICAD 2021 - Medical Imaging and Computer-Aided Diagnosis

A2 - Su, Ruidan

A2 - Zhang, Yu-Dong

A2 - Liu, Han

PB - Springer Science and Business Media Deutschland GmbH

T2 - International Conference on Medical Imaging and Computer-Aided Diagnosis, MICAD 2021

Y2 - 25 March 2021 through 26 March 2021

ER -

Perlo D, Tartaglione E, Bertero L, Cassoni P, Grangetto M. Dysplasia Grading of Colorectal Polyps Through Convolutional Neural Network Analysis of Whole Slide Images. Dans Su R, Zhang YD, Liu H, rédacteurs en chef, Proceedings of 2021 International Conference on Medical Imaging and Computer-Aided Diagnosis, MICAD 2021 - Medical Imaging and Computer-Aided Diagnosis. Springer Science and Business Media Deutschland GmbH. 2022. p. 325-334. (Lecture Notes in Electrical Engineering). doi: 10.1007/978-981-16-3880-0_34

Dysplasia Grading of Colorectal Polyps Through Convolutional Neural Network Analysis of Whole Slide Images

Résumé

Série de publications

Une conférence

SDG des Nations Unies

Accès au document

Empreinte digitale

Contient cette citation