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.

This paper presents a versatile SAR ADC targeting the acquisition of signals in biomedical applications. The converter is implemented in a CMOS AMS 0.35µm technology and powered with 3.3V. Its output bit-width can be varied from 4 to 8 bits. The device includes built-in programmable clock generator and voltage reference circuit. The sampling rate can be tuned up to 100kS/s in the 8-bit mode and with a maximal input range of 2.5V. All these parameters can be independently adjusted. This is an unique fully-configurable converter. The simulation results are reported and positioned with respect to the most recent designs.

Versatile SAR-ADC for Biomedical Applications

Most image retrieval methods use global features that aggregate local distinctive patterns into a single representation. However, the aggregation process destroys the relative spatial information by considering orderless sets of local descriptors. We propose to integrate relative spatial information into the aggregation process by taking into account co-occurrences of local patterns in a tensor framework. The resulting signature called Improved Spatial Tensor Aggregation (ISTA) is able to reach state of the art performances on well known datasets such as Holidays, Oxford5k and Paris6k.

Leveraging Implicit Spatial Information in Global Features for Image Retrieval

Brain organoids are three-dimensional tissues gener-ated in vitro from pluripotent stem cells and replicating the early development of Human brain. To implement, test and compare methods to follow their growth on microscopic images, a large dataset not always available is required with a trusted ground truth when developing automated Machine Learning solutions. Recently, optimized Generative Adversarial Networks prove to generate only a similar object content but not a background specific to the real acquisition modality. In this work, a small database of brain organoid bright field images, characterized by a shot noise background, is extended using the already validated AAEGAN architecture, and specific noise or a mixture noise injected in the generator. We hypothesize this noise injection could help to generate an homogeneous and similar bright-field background. To validate or invalidate our generated images we use metric calculation, and a dimensional reduction on features on original and generated images. Our result suggest that noise injection can modulate the generated image backgrounds in order to produce a more similar content as produced in the microscopic reality. A validation of these images by biological experts could augment the original dataset and allow their analysis by Deep-based solutions.

AAEGAN Optimization by Purposeful Noise Injection for the Generation of Bright-Field Brain Organoid Images

Capsule endoscopy (CE) is a valid alternative to conventional gastrointestinal (GI) endoscopy tools. In CE, annotation tools are crucial in developing large and annotated medical image databases for training deep neural networks (DNN). We provide an overview of the described and in-use various annotation systems available, focusing on the annotation of adenomatous polyp pathology in the GI tract. Some studies present promising results regarding time efficiency by implementing automated labelling features in annotation systems. Thus, data are inadequate regarding the general overview for users, and may also be more specific on which features provided are necessary for polyp annotation.

/pdf/annotation-tools-in-gastrointestinal-polyp-annotation-1ein6ffi.pdf

Annotation Tools in Gastrointestinal Polyp Annotation

Detecting anomalies using deep learning has become a major challenge over the last years, and is becoming increasingly promising in several fields. The introduction of self-supervised learning has greatly helped many methods including anomaly detection where simple geometric transformation recognition tasks are used. However these methods do not perform well on fine-grained problems since they lack finer features. By combining in a multi-task framework high-scale shape features oriented task with low-scale fine features oriented task, our method greatly improves fine-grained anomaly detection. It outperforms state-of-the-art with up to 31% relative error reduction measured with AUROC on various anomaly detection problems.

Aymeric Histace

Papers

Versatile SAR-ADC for Biomedical Applications

Leveraging Implicit Spatial Information in Global Features for Image Retrieval

AAEGAN Optimization by Purposeful Noise Injection for the Generation of Bright-Field Brain Organoid Images

Annotation Tools in Gastrointestinal Polyp Annotation

Fine-grained Anomaly Detection via Multi-task Self-Supervision