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Constrained multiple instance learning for ulcerative colitis prediction using histological images

delete2022-09-01
delete16
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OA
AI
R
Rocío del Amor *
P
Pablo Meseguer
T
Tommaso Lorenzo Parigi
V
Vincenzo Villanacci
A
Adrián Colomer
L
Laëtitia Launet
A
Alina Bazarova
G
Gian Eugenio Tontini
R
Raf Bisschops
G
Gert De Hertogh
J
José G. Ferraz
M
Martín Götz
X
Xianyong Gui
H
Hayee, Bu'Hussain
M
Mark Lazarev
R
Remo Panaccione
A
Adolfo Parra‐Blanco
P
Pradeep Bhandari
L
Luca Pastorelli
T
Timo Räth
E
Elin Synnøve Røyset
M
Michael Vieth
D
Davide Zardo
E
Enrico Grisan
S
Subrata Ghosh
M
Marietta Iacucci
V
Valery Naranjo
DOI:10.1016/j.cmpb.2022.107012delete
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Abstract

Abstract

En 中文
Background and Objective: Ulcerative colitis (UC) is an inflammatory bowel disease (IBD) affecting the colon and the rectum characterized by a remitting-relapsing course. To detect mucosal inflammation as-sociated with UC, histology is considered the most stringent criteria. In turn, histologic remission (HR) correlates with improved clinical outcomes and has been recently recognized as a desirable treatment target. The leading biomarker for assessing histologic remission is the presence or absence of neutrophils. Therefore, the finding of this cell in specific colon structures indicates that the patient has UC activity. However, no previous studies based on deep learning have been developed to identify UC based on neu-trophils detection using whole-slide images (WSI). Methods: The methodological core of this work is a novel multiple instance learning (MIL) framework with location constraints able to determine the presence of UC activity using WSI. In particular, we put forward an effective way to introduce constraints about positive instances to effectively explore additional weakly supervised information that is easy to obtain and enjoy a significant boost to the learning process. In addition, we propose a new weighted embedding to enlarge the relevance of the positive instances. Results: Extensive experiments on a multi-center dataset of colon and rectum WSIs, PICASSO-MIL, demon-strate that using the location information we can improve considerably the results at WSI-level. In com-parison with prior MIL settings, our method allows for 10% improvements in bag-level accuracy. Conclusion : Our model, which introduces a new form of constraints, surpass the results achieved from current state-of-the-art methods that focus on the MIL paradigm. Our method can be applied to other histological concerns where the morphological features determining a positive WSI are tiny and similar to others in the image. (c) 2022 Elsevier B.V. All rights reserved.
Keywords:
Histologic remission
Location constraints
Neutrophils
Attention -embedding weights
Ulcerative colitis
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Computer Methods and Programs in Biomedicine cover
Computer Methods and Programs in Biomedicine
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