MultiResUNet : Rethinking the U-Net architecture for multimodal biomedical image segmentation.

Photoplethysmography and its application in clinical physiological measurement

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.

Philosophical Transactions of the Royal Society of London. for the Year Mdcccxxvii. Part I. Volume Cxvii:355–388, 1827

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.

A regularity statistic for medical data analysis

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.

https://gut.bmj.com/content/gutjnl/68/10/1813.full.pdf

Real-time automatic detection system increases colonoscopic polyp and adenoma detection rates: a prospective randomised controlled study.

La detection d'un objet dans des images bruitees est etudiee au moyen d'une mesure de contraste basee sur la divergence de Renyi. L'analyse etend, et contient comme un cas particulier, les travaux anterieurs realises sur une comparaison pour cette meme tâche de detection de la divergence de Kullback-Leibler avec celle de Bhattacharrya. L'extension porte sur l'etude de l'influence du parametre de la divergence de Renyi, l'echelle d'observation, et le type de bruits testes en incluant d'autres familles de bruits d'interets pratiques en imagerie.

https://hal.archives-ouvertes.fr/hal-01169034/document

Divergence de Rényi comme mesure de contraste pour la détection d'objets dans des images bruitées

A simple method for accurately identifying Glossina spp in the field is a challenge to sustain the future elimination of Human African Trypanosomiasis (HAT) as a public health scourge, as well as for the sustainable management of African Animal Trypanosomiasis (AAT). Current methods for Glossina species identification heavily rely on a few well-trained experts. Methodologies that rely on molecular methodologies like DNA barcoding or mass spectrometry protein profiling (MALDI TOFF) haven't been thoroughly investigated for Glossina sp. Nevertheless, because they are destructive, costly, time-consuming, and expensive in infrastructure and materials, they might not be well adapted for the survey of arthropod vectors involved in the transmission of pathogens responsible for Neglected Tropical Diseases, like HAT. This study demonstrates a new type of methodology to classify Glossina species. In conjunction with a deep learning architecture, a database of Wing Interference Patterns (WIPs) representative of the Glossina species involved in the transmission of HAT and AAT was used. This database has 1766 pictures representing 23 Glossina species. This cost-effective methodology, which requires mounting wings on slides and using a commercially available microscope, demonstrates that WIPs are an excellent medium to automatically recognize Glossina species with very high accuracy. 

/pdf/wing-interferential-patterns-wips-and-machine-learning-a-2a0qoncs.pdf

Wing Interferential Patterns (WIPs) and machine learning, a step toward automatized tsetse (Glossina spp.) identification

Development of a Computed Cleansing Score to Assess the Quality of Bowel Preparation in Colon Capsule Endoscopy

Système et procédé d'identification automatisée de diptères hématophages

Cet article presente une methode de segmentation par contours actifs bases histogramme integrant comme mesure de similarite la famille particuliere des alpha-divergences. L'interet principal de cette methode reside (i) dans la flexibilite des alpha-divergences dont la metrique intrinseque peut-etre parametrisee via la valeur de alpha et donc adaptee aux distributions statistiques des regions de l'image a segmenter ; et (ii) dans la capacite unificatrice de cette mesure statistique vis-a-vis des distances classiquement utilisees dans ce contexte (Kullback- Leibler, Hellinger...). Nous abordons l'etude de cette mesure statistique tout d'abord d'un point de vue supervise pour lequel le processus iteratif de segmentation se deduit de la minimisation de l'alpha -divergence entre la densite de probabilite courante et une reference definie manuellement. Puis nous nous focalisons sur le point de vue non supervise qui permet de se dedouaner de l'etape de definition des references par le biais d'une maximisation de distance entre les densites de probabilites interieure et exterieure au contour. Par ailleurs, nous proposons une demarche d'optimisation de l'evolution du parametre alpha conjointe au processus d'extremisation de la divergence, permettant d'adapter iterativement la divergence a la statistique des donnees considerees. Au niveau experimental, nous proposons une etude comparee des differentes approches de segmentations : en premier lieu, sur des images synthetiques bruitees et texturees, puis, sur des images naturelles. Enfin, nous focalisons notre etude sur differentes applications issues des domaines biomedicaux (microscopie confocale cellulaire) et medicaux (radiographie X) dans le contexte de l'aide au diagnotic. Dans chacun des cas, une discussion sur l'apport des alpha-divergences est proposee.

https://hal.archives-ouvertes.fr/hal-00732364/document

Aymeric Histace

Papers

Divergence de Rényi comme mesure de contraste pour la détection d'objets dans des images bruitées

Wing Interferential Patterns (WIPs) and machine learning, a step toward automatized tsetse (Glossina spp.) identification

Development of a Computed Cleansing Score to Assess the Quality of Bowel Preparation in Colon Capsule Endoscopy

Système et procédé d'identification automatisée de diptères hématophages

Alpha-divergences pour la segmentation d'images par contours actifs basés histogrammes : Application à l'analyse d'images médicales et biomédicales