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Aymeric Histace

Bio: Aymeric Histace is an academic researcher from École nationale supérieure de l'électronique et de ses applications. The author has contributed to research in topics: Active contour model & Segmentation. The author has an hindex of 14, co-authored 116 publications receiving 1126 citations. Previous affiliations of Aymeric Histace include University of Angers & Centre national de la recherche scientifique.


Papers
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Journal ArticleDOI
01 Mar 2014
TL;DR: A new embeddable method for polyp detection in wireless capsule endoscopic images was developed and tested using boosting based approach that achieved good classification performance and can be implemented in situ with embedded hardware.
Abstract: Purpose Wireless capsule endoscopy (WCE) is commonly used for noninvasive gastrointestinal tract evaluation, including the detection of mucosal polyps. A new embeddable method for polyp detection in wireless capsule endoscopic images was developed and tested.

491 citations

Journal ArticleDOI
TL;DR: Results show that convolutional neural networks are the state of the art in polyp detection and it is also demonstrated that combining different methodologies can lead to an improved overall performance.
Abstract: Colonoscopy is the gold standard for colon cancer screening though some polyps are still missed, thus preventing early disease detection and treatment. Several computational systems have been proposed to assist polyp detection during colonoscopy but so far without consistent evaluation. The lack of publicly available annotated databases has made it difficult to compare methods and to assess if they achieve performance levels acceptable for clinical use. The Automatic Polyp Detection sub-challenge, conducted as part of the Endoscopic Vision Challenge ( http://endovis.grand-challenge.org ) at the international conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) in 2015, was an effort to address this need. In this paper, we report the results of this comparative evaluation of polyp detection methods, as well as describe additional experiments to further explore differences between methods. We define performance metrics and provide evaluation databases that allow comparison of multiple methodologies. Results show that convolutional neural networks are the state of the art. Nevertheless, it is also demonstrated that combining different methodologies can lead to an improved overall performance.

331 citations

Proceedings ArticleDOI
27 Oct 2019
TL;DR: This paper tackles the scattering problem with a distribution-aware regularization named HORDE, which enforces visually-close images to have deep features with the same distribution which are well localized in the feature space.
Abstract: Learning an effective similarity measure between image representations is key to the success of recent advances in visual search tasks (e.g. verification or zero-shot learning). Although the metric learning part is well addressed, this metric is usually computed over the average of the extracted deep features. This representation is then trained to be discriminative. However, these deep features tend to be scattered across the feature space. Consequently, the representations are not robust to outliers, object occlusions, background variations, etc. In this paper, we tackle this scattering problem with a distribution-aware regularization named HORDE. This regularizer enforces visually-close images to have deep features with the same distribution which are well localized in the feature space. We provide a theoretical analysis supporting this regularization effect. We also show the effectiveness of our approach by obtaining state-of-the-art results on 4 well-known datasets (Cub-200-2011, Cars-196, Stanford Online Products and Inshop Clothes Retrieval).

73 citations

Book ChapterDOI
14 Sep 2017
TL;DR: A strategy to adapt real-time polyps detection methods to video analysis by adding a spatio-temporal stability module and studying a combination of features to capture polyp appearance variability is proposed.
Abstract: Colorectal cancer is the second cause of cancer death in United States: precursor lesions (polyps) detection is key for patient survival. Though colonoscopy is the gold standard screening tool, some polyps are still missed. Several computational systems have been proposed but none of them are used in the clinical room mainly due to computational constraints. Besides, most of them are built over still frame databases, decreasing their performance on video analysis due to the lack of output stability and not coping with associated variability on image quality and polyp appearance. We propose a strategy to adapt these methods to video analysis by adding a spatio-temporal stability module and studying a combination of features to capture polyp appearance variability. We validate our strategy, incorporated on a real-time detection method, on a public video database. Resulting method detects all polyps under real time constraints, increasing its performance due to our adaptation strategy.

70 citations


Cited by
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Journal ArticleDOI
TL;DR: This work develops a novel architecture, MultiResUNet, as the potential successor to the U-Net architecture, and tests and compared it with the classical U- net on a vast repertoire of multimodal medical images.

1,027 citations

Journal ArticleDOI
TL;DR: This book is coming as the best seller book today and when you are really a good reader or you're fans of the author, it does will be funny if you don't have this book.
Abstract: Follow up what we will offer in this article about philosophical transactions of the royal society of london series b biological sciences no 600 vol 233 studies of the post glacial history of british vegetation x correlation between climate forest composition prehistoric agriculture and peat st. You know really that this book is coming as the best seller book today. So, when you are really a good reader or you're fans of the author, it does will be funny if you don't have this book. It means that you have to get this book. For you who are starting to learn about something new and feel curious about this book, it's easy then. Just get this book and feel how this book will give you more exciting lessons.

607 citations

01 Jan 1991
TL;DR: In this article, a new statistic called approximate entropy (ApEn) was developed to quantify the amount of regularity in data, which has potential application throughout medicine, notably in electrocardiogram and related heart rate data analyses and in the analysis of endocrine hormone release pulsatility.
Abstract: A new statistic has been developed to quantify the amount of regularity in data. This statistic, ApEn (approximate entropy), appears to have potential application throughout medicine, notably in electrocardiogram and related heart rate data analyses and in the analysis of endocrine hormone release pulsatility. The focus of this article is ApEn. We commence with a simple example of what we are trying to discern. We then discuss exact regularity statistics and practical difficulties of using them in data analysis. The mathematic formula development for ApEn concludes the Solution section. We next discuss the two key input requirements, followed by an account of a pilot study successfully applying ApEn to neonatal heart rate analysis. We conclude with the important topic of ApEn as a relative (not absolute) measure, potential applications, and some caveats about appropriate usage of ApEn. Appendix A provides example ApEn and entropy computations to develop intuition about these measures. Appendix B contains a Fortran program for computing ApEn. This article can be read from at least three viewpoints. The practitioner who wishes to use a "black box" to measure regularity should concentrate on the exact formula, choices for the two input variables, potential applications, and caveats about appropriate usage. The physician who wishes to apply ApEn to heart rate analysis should particularly note the pilot study discussion. The more mathematically inclined reader will benefit from discussions of the relative (comparative) property of ApEn and from Appendix A.

508 citations

Journal ArticleDOI
01 Oct 2019-Gut
TL;DR: In a low prevalent ADR population, an automatic polyp detection system during colonoscopy resulted in a significant increase in the number of diminutive adenomas detected, as well as an increase inThe rate of hyperplastic polyps.
Abstract: Objective The effect of colonoscopy on colorectal cancer mortality is limited by several factors, among them a certain miss rate, leading to limited adenoma detection rates (ADRs). We investigated the effect of an automatic polyp detection system based on deep learning on polyp detection rate and ADR. Design In an open, non-blinded trial, consecutive patients were prospectively randomised to undergo diagnostic colonoscopy with or without assistance of a real-time automatic polyp detection system providing a simultaneous visual notice and sound alarm on polyp detection. The primary outcome was ADR. Results Of 1058 patients included, 536 were randomised to standard colonoscopy, and 522 were randomised to colonoscopy with computer-aided diagnosis. The artificial intelligence (AI) system significantly increased ADR (29.1%vs20.3%, p Conclusions In a low prevalent ADR population, an automatic polyp detection system during colonoscopy resulted in a significant increase in the number of diminutive adenomas detected, as well as an increase in the rate of hyperplastic polyps. The cost–benefit ratio of such effects has to be determined further. Trial registration number ChiCTR-DDD-17012221; Results.

494 citations