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Understanding Images from Pixel Level with Semantic Segmentation - DeepLobe

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Image Segmentation is considered a vital task in Computer Vision – along with Object Detection – as it involves understanding what is given in the image at a pixel level. It provides a comprehensive description that includes the information of the object, category, position, and shape of the given image. There are various algorithms for Image Segmentation that have been developed with applications such as scene understanding, medical image analysis, robotics, augmented reality, video surveillance, etc. The advent of Deep Learning in Computer Vision has diversified the capabilities of the existing algorithms and paved the way for new algorithms for pixel-level labeling problems such as Semantic Segmentation. These algorithms learn rich representations for the problem, including automatic pixel labeling of images in an end-to-end fashion.


The AI Behind DeepMind's AlphaFold - and its Implications for the Future of Drug Discovery

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Kristóf is Founder and CTO at Turbine.AI, and holds a PhD in molecular biology and bioinformatics. To inquire about contributed articles from outside experts, contact editorial@emerj.com. Could you predict how an airplane flies only based on an inventory of its parts? This – with proteins – is the essence of the protein folding challenge. Two weeks ago, the organizers of the CASP protein folding challenge just announced that DeepMind's AlphaFold essentially solved the challenge – its prediction score was just below experimental error.


How AI in Healthcare Is Changing the Industry

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AI in healthcare is something that is revolutionizing the industry and medical treatment that we as the patients receive. But AI, in general, is making inroads into virtually every field and aspect of society. Healthcare AI companies like NVIDIA healthcare and Google DeepMind Health are breaking new ground, with innovations that are helping to save lives. Let's dive into the world of AI so that you can have a better understanding of what it is all about and where it is going. AI stands for artificial intelligence.


AI Experts on Different Language NLP Datasets in APAC

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Regarding the issue of different languages, generally speaking, biomedical NLP targets the languages of the scientific literature and the language of documentation in electronic health records. For the former, while much of the scientific literature is in English, it definitely isn't all, and I have been involved with efforts to work on automatic machine translation specifically for scientific texts, specifically through the Workshop on Machine Translation Biomedical task. For the latter, a key challenge is the availability of data sets and resources for working with clinical texts in different languages; clinical texts are not easy to obtain in any language. However, there are ongoing efforts to make these available, for instance for Spanish, the Biomedical Text Mining Unit at the Barcelona Supercomputing Center has run several shared tasks on Spanish-language clinical texts, and I collaborated with a team to develop a deep learning-based NLP approach for named entity recognition in Spanish clinical narratives in that context. Another challenge is'translating' complex clinical terminology to more consumer-friendly language; we have also done some early work leveraging Wikipedia for that (called WikiUMLS).


PDBench: Evaluating Computational Methods for Protein Sequence Design

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Proteins perform critical processes in all living systems: converting solar energy into chemical energy, replicating DNA, as the basis of highly performant materials, sensing and much more. While an incredible range of functionality has been sampled in nature, it accounts for a tiny fraction of the possible protein universe. If we could tap into this pool of unexplored protein structures, we could search for novel proteins with useful properties that we could apply to tackle the environmental and medical challenges facing humanity. This is the purpose of protein design. Sequence design is an important aspect of protein design, and many successful methods to do this have been developed.


Did DeepMind just make a big step toward more human-like A.I.? – Fortune

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This is the web version of Eye on A.I., Fortune's weekly newsletter covering artificial intelligence and business. To get it delivered weekly to your in-box, sign up here. In January 2020, in a Fortune magazine cover story, I chronicled the corporate race for artificial general intelligence, a kind of human-like or even superhuman A.I. that is the staple of science fiction. The pursuit of AGI, as it's more commonly called, has led to many of the machine learning innovations that underpin the current A.I. boom. But that boom is centered around narrow A.I--software that can perform one, specific task well.


Deep learning detects heart failure with preserved ejection fraction using a baseline electrocardiogram

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This study included two patient cohorts. In the derivation cohort, we included n 1884 patients who presented with exertional dyspnea or equivalent and preserved ejection fraction ( 50%) and clinical suspicion for coronary artery disease. The ECGs were divided in segments, yielding a total of 77.558 samples. We trained a convolutional neural network (CNN) to classify HFpEF and control patients according to ESC criteria. An external group of 203 volunteers in a prospective heart failure screening program served as validation cohort of the CNN.


NVIDIA Clara Imaging

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Today, GPUs are found in almost all imaging modalities, including CT, MRI, x-ray, and ultrasound - bringing compute capabilities to the edge devices. With the boom of deep learning research in medical imaging, more efficient and improved approaches are being developed to enable AI-assisted workflows. To develop these AI capable applications, the data needs to be made AI-ready. NVIDIA Clara's AI-Assisted Annotation does so by providing APIs and a toolkit to bring AI-assisted annotation capabilities to any medical viewer. Post annotation, data scientists and researchers need to build a robust AI model.


Initialization methods of convolutional neural networks for detection of image manipulations

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Fake images and videos have engulfed mass communication media. This is not something recent, manipulations and forgeries have occurred since the advent of photography itself. These alterations can go from innocent retouches in an attempt to make an image visually attractive to the spread of misleading information or even the use of false media in legal instances. Accordingly, the creation of methods that can help us assure the authenticity of an image presented as non-modified is of paramount importance. In this thesis, we aim at detecting image manipulation operations using deep learning techniques. We present three methods showing the progression of our work under one common objective, i.e, the design and test of Convolutional Neural Network (CNN) initialization methods for image forensic problems with a variance stability focus for the output of a CNN layer.First, we carry out an extensive review of the state of the art in deep-learning-based methods for image forensics. From this review we can confirm that the first layer of a CNN has big impact on the final performance. Specifically, the initialization used on the first-layer filters plays an important role that should be in line with the image forensic task in hand.As our first attempt to address this research problem, we propose a low-complexity initialization method for CNNs. Taking advantage of previous methods designed for the computer vision field, we extend the popular Xavier method to design a filter that would provide variance stability after a convolution operation. This method generates a set of random high-pass filters for the initialization of a CNN's first layer. These filters allow us to better identify forensic traces which usually lie towards the high-frequency part of the image.This first approach constitutes a good staring point of our work. However, a wrong assumption, largely utilized in the research community, was made. This is corrected in our second method where we follow a different data-dependent approach and take into consideration the real statistical properties of natural images. Accordingly, we propose a scaling method for first-layer filters which can cope well with different CNN initialization algorithms. The objective remains in keeping the stability of the variance of data flow in a CNN. We also present theoretical and experimental studies on the output variance for convolutional filter, which are the basis of our proposed data-dependent scaling.Next we describe a revisited version of our first proposal now with a corrected assumption on the statistics of natural images. More precisely, we propose an improved random high-pass initialization method which does not explicitly compute the statistics of input data. We believe that such a ``data-independent'' approach has higher flexibility and broader application range than our second method in situations where the computation of input statistics is not possible.Our proposed methods are tested over several image forensic problems and different CNN architectures.Finally, during all this thesis work we took part in a challenge competition of image forgery detection organized by the French National Research Agency and the French Directorate General of Armaments. We explain in the Appendix the objectives of the challenge along with a brief description of our work conducted for the competition.


Bringing TrackMate in the era of machine-learning and deep-learning

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TrackMate is an automated tracking software used to analyze bioimages and distributed as a Fiji plugin. Here we introduce a new version of TrackMate rewritten to improve performance and usability, and integrating several popular machine and deep learning algorithms to improve versatility. We illustrate how these new components can be used to efficiently track objects from brightfield and fluorescence microscopy images across a wide range of bio-imaging experiments. Object tracking is an essential image analysis technique used across biosciences to quantify dynamic processes. In life sciences, tracking is used for instance to track single particles, sub-cellular organelles, bacteria, cells, and whole animals.