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Conference papers

CrohnIPI: An endoscopic image database for the evaluation of automatic Crohn's disease lesions recognition algorithms

Abstract : Wireless capsule endoscopy (WCE) allows medical doctors to examine the interior of the small intestine with a noninvasive procedure. This methodology is particularly important for Crohn's disease (CD), where an early diagnosis improves treatment outcomes. The counting and identification of CD lesions in WCE videos is a time-consuming process for medical experts. In the era of deep-learning many automatic WCE lesion classifiers, requiring annotated data, have been developed. However, benchmarking classifiers is difficult due to the lack of standard evaluation data. Most detection algorithms are evaluated on private datasets or on unspecified subsets of public databases. To help the development and comparison of automatic CD lesion classifiers, we release CrohnIPI, a dataset of 3498 images, independently reviewed by several experts. It contains 60.55% of non-pathological images and 38.85% of pathological images with 7 different types of CD lesions. A part of these images are multilabeled. The dataset is balanced between pathological images and non-pathological ones and split into two subsets for training and testing models. This database will progressively be enriched over the next few years in aim to make the automatic detection algorithms converge to the most accurate system possible and to consolidate their evaluation.
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https://hal.archives-ouvertes.fr/hal-02518263
Contributor : Harold Mouchère Connect in order to contact the contributor
Submitted on : Friday, November 20, 2020 - 3:54:18 PM
Last modification on : Wednesday, April 27, 2022 - 3:52:09 AM

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Rémi Vallée, Astrid de Maissin, Antoine Coutrot, Harold Mouchère, Arnaud Bourreille, et al.. CrohnIPI: An endoscopic image database for the evaluation of automatic Crohn's disease lesions recognition algorithms. SPIE Medical Imaging, Feb 2020, Houston, France. pp.61, ⟨10.1117/12.2543584⟩. ⟨hal-02518263⟩

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