Constrained multiple instance learning for ulcerative colitis prediction using histological images
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Author
Del Amor, RocíoMeseguer, Pablo
Parigi, Tommaso Lorenzo
Villanacci, Vincenzo
Colomer, Adrián
Launet, Laëtitia
Bazarova, Alina
Tontini, Gian Eugenio
Bisschops, Raf
De Hertogh, Gert
Ferraz, Jose G
Götz, Martin
Gui, Xianyong
Hayee, Bu'Hussain
Lazarev, Mark
Panaccione, Remo
Parra-Blanco, Adolfo
Bhandari, Pradeep
Pastorelli, Luca
Rath, Timo
Røyset, Elin Synnøve
Vieth, Michael
Zardo, Davide
Grisan, Enrico
Ghosh, Subrata
Iacucci, Marietta
Naranjo, Valery
Publication date
2022-07-09
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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 associated 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 neutrophils 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, demonstrate that using the location information we can improve considerably the results at WSI-level. In comparison 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.Citation
Del Amor R, Meseguer P, Parigi TL, Villanacci V, Colomer A, Launet L, Bazarova A, Tontini GE, Bisschops R, de Hertogh G, Ferraz JG, Götz M, Gui X, Hayee B, Lazarev M, Panaccione R, Parra-Blanco A, Bhandari P, Pastorelli L, Rath T, Røyset ES, Vieth M, Zardo D, Grisan E, Ghosh S, Iacucci M, Naranjo V. Constrained multiple instance learning for ulcerative colitis prediction using histological images. Comput Methods Programs Biomed. 2022 Sep;224:107012. doi: 10.1016/j.cmpb.2022.107012. Epub 2022 Jul 9.Type
ArticlePMID
35843078Publisher
Elsevierae974a485f413a2113503eed53cd6c53
10.1016/j.cmpb.2022.107012