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Further Clinical Implementation of Deep Learning Segmentation

further clinical implementation of deep learning segmentation

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RaySearch Laboratories AB (publ) announces that its advanced deep learning segmentation functionality for lung delineation in breast radiation therapy has been carried out using treatment planning system RayStation® * at Leeds Cancer Centre (LCC) in the UK.Clinicians at LCC have successfully implemented the deep learning segmentation thorax solution available in RayStation following thorough investigation of the geometric and dosimetric accuracy of the generated organs-at-risk structures. Reference contours and deep learning segmentation contours were produced for 10 patients, including left- and right-sided breast and chest wall treatments. A robust auto-contouring evaluation and commissioning method, based on templates and scripted analysis within RayStation 8B, has been developed as a result.
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The in-built deep learning segmentation thorax model in RayStation 8B provides fast and excellent performance for ipsilateral lung contouring in the hypofractionated breast RT scenario. Given the positive results, the medical physics department at LCC is working closely with RaySearch to develop other machine learning models for safe and effective clinical implementation for other treatment sites. LCC staff are developing a script-driven quality management workflow for both deep learning segmentation model training on local data and semi-automated evaluation. This approach will allow …

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Deep learning enables early detection and classification of live bacteria using holography

deep learning enables early detection and classification of live bacteria using holography

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Deep learning-based early detection and classification of live bacteria. a, Schematic of the device. b, Whole plate image of E. coli and K. aerogenes colonies. c, Example images of the individual growing bacterial colonies detected by a trained deep neural network. The time points of the detection and classification of growing colonies are annotated with blue arrows. The scale bar is 0.1 mm. Credit: UCLA Engineering Institute for Technology Advancement

Waterborne diseases affect more than 2 billion people worldwide, causing substantial economic burden. For example, the treatment of waterborne diseases costs more than $2 billion annually in the United States alone, with 90 million cases recorded per year. Among waterborne pathogen-related problems, one of the most common public health concerns is the presence of total coliform bacteria and Escherichia coli (E. coli) in drinking water, which indicates fecal contamination. Traditional culture-based bacteria detection methods often take 24-48 hours, followed by visual inspection and colony counting by an expert, according to the United States Environmental Protection Agency (EPA) guidelines. Alternatively, molecular detection methods based on, for example, the amplification of nucleic acids, can reduce the detection time to a few hours, but they generally lack the sensitivity for detecting bacteria at very low concentrations, and …

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Disrupting Quantum Computing With AI and Machine Learning

disrupting quantum computing with ai and machine learning

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Quantum Computing is approaching a period of commercialization that may change our reality. Early adopters of quantum’s remarkable capacity to take care of specific kinds of issues may accomplish achievements that empower new business models. Visionary enterprises are now lining up with the developing quantum computing ecosystem to become “quantum ready.” These ground breaking enterprises are exploring use cases and related algorithms that address complex business issues.
Artificial Intelligence (AI) and Machine Learning (ML) based analytics solutions require aggregating and analysing data to train them to copy real-world observed behaviours. For industrial solutions, it begins with a historian that totals time-series and event data. This data is taken care of into an analytics engine that models the data utilizing proprietary algorithms, combining data science and field operator expertise.
The bits of insights are introduced to the end-user, who takes explicit activities and records those activities. The application currently ‘learns’ in light of real-time activities. After some time, we begin seeing a convergence, which guarantees accurate decision-making at the operator level.
When quantum PCs can take care of some business issues that traditional PCs can’t, often called quantum advantage, shows up close within reach. Absolutely when a quantum advantage …

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‘Make in India’: Artificial Intelligence Company, AiBridge ML, Adds Handwriting and Image Recognition Capabilities to AiMunshi, the Popular Financial Document Automation Tool

‘make in india’: artificial intelligence company, aibridge ml, adds handwriting and image recognition capabilities to aimunshi, the popular financial document automation tool

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HYDERABAD, India, July 10, 2020 /PRNewswire/ — Today, Founder & Chief Data Scientist of AiBridge ML, Mr. Prajnajit Mohanty, announced addition of Handwriting & Image Recognition capabilities to their Financial Document Automation tool, AIMunshi. It is notable that AIMunshi is a ‘Make in India’ Deep Learning based Intelligent Financial Documents Automation tool from AiBridge ML.
“Addition of deep learning based Handwriting & Image recognition capabilities to AiMunshi will enable us to offer augmented features to diversified industries. It will help them to operate in contactless manner and automate their routine work during current COVID-19 pandemic. Industries like education, healthcare, retail, manufacturing etc. will be benefitted immensely and we are committed to help Indian industries to use AI & Machine learning,” said Mr. Mohanty.
Many USA and Australian healthcare, pharma and retail companies have already realized considerable financial and operational benefits using AiMunshi and yielding real, tangible ROI faster.
AiMunshi processes orders and invoices automatically, reducing accounts payable costs while improving both the accuracy and the speed of data extraction from various sources or emails directly. It is capable of automatically interpreting the relevant information and fields within a PDF or image-based invoices and order, or in emails in real-time.
Intelligent features of AiMunshi:

Contactless Orders & Invoices …

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Deep Learning System Market Size Current and Future Industry Trends, 2020-2025

deep learning system market size current and future industry trends, 2020-2025

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The Deep Learning System report provides independent information about the Deep Learning System industry supported by extensive research on factors such as industry segments size & trends, inhibitors, dynamics, drivers, opportunities & challenges, environment & policy, cost overview, porter’s five force analysis, and key companies profiles including business overview and recent development.
The research report on Deep Learning System market thoroughly investigates historical data of this business sphere to lay out the future roadmap of the industry. The study attempts to predict a long-term picture of the market scenario with respect to the various growth indicators, hindrances, and opportunities that determine the industry expansion. Moreover, the report provides an exhaustive synopsis of the industry at a global and regional level. In addition, it covers the impact of COVID-19 pandemic on the leading industry players and various market segmentations.
Deep Learning System Market rundown:
Request Sample Copy of this Report @ https://www.aeresearch.net/request-sample/240504An overview of the regional outlook of the Deep Learning System market:

As per the report, the regional landscape of the Deep Learning System market is fragmented into North America, Europe, Asia Pacific, Latin America, Middle East and Africa.
The report imparts figures pertaining to the market share …

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COVID-19 Impact & Recovery Analysis – Artificial Intelligence Platforms Market 2020-2024 | Rise in Demand for AI-based Solutions to Boost Growth | Technavio

covid-19 impact & recovery analysis – artificial intelligence platforms market 2020-2024 | rise in demand for ai-based solutions to boost growth | technavio

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The Global Artificial Intelligence Platforms Market will grow by $ 12.51 bn during 2020-2024

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efficacyAI Signs Exclusive Software License for MedicascyAI™ a Suite of Deep Learning Algorithms Used to Predict Drug Safety and Efficacy

iam platform login page background

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efficacyAI, Inc. announced today that it has recently signed a license agreement with the Georgia Tech Research Corporation granting it exclusive righ

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The Most Common Misconceptions About Artificial Intelligence

the most common misconceptions about artificial intelligence

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In a world where big data, automation, and neural networks have become everyday parlance, misconceptions about artificial intelligence and the processes behind it are spreading like wildfire. Naturally, the vast and unprecedented potential applications of AI tend to generate a lot of buzz, particularly where the economy is concerned.
However, all-too-often people tend to mischaracterize or misunderstand what AI is all about, which only serves to undermine its potential as a liberating technology. Let’s clear up the most common AI misconceptions, in order to have a more grounded understanding of this emerging technology and its potential use cases.

1. “AI will take away my job”
Arguably the most widespread and potentially dangerous misconception about artificial intelligence is that it will take away jobs from humans. Yes, automation is leading to the increased redundancy of a number of certain low-skilled jobs, but this trend has been significantly overblown in recent years. In addition, most scientific estimates demonstrate that AI-driven automation will likely create more jobs than it will displace. However, even this is beside the point. AI in the workplace has the power to improve how people and businesses perform their jobs, rather than removing the need for humans to do …

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Viral Post Highlights ‘Toxicity Problems’ in the Machine Learning Community

viral post highlights ‘toxicity problems’ in the machine learning community

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A Reddit post identifying eight “toxicity problems” in the machine learning (ML) community recently went viral, receiving some 3,300 upvotes and nearly 600 comments in a week.The post highlights perceived peer-review problems, the reproducibility crisis, and ethics and diversity issues. It arguing that the peer-review process is “broken” and that there is a “worshiping problem” and “a cut-throat publish-or-perish mentality” in the paper publishing process and beyond.Over 60 percent of published theoretical computer science and machine learning papers are on arXiv, according to a 2017 study. Indeed, 56 percent of papers published in 2017 appeared on arXiv (along with the authors’ names and institutions) before or during peer review. The Reddit post says this can negatively affect the double-blind peer review process, as reviewers could be more inclined to accept papers whose authors are from renowned institutions.Earlier this year, Synced also looked at some possible ways to improve the paper review process in the ML community. Since 1998, the volume of AI papers in peer-reviewed journals has grown by more than 300 percent, according to the AI Index 2019 Report. At the same time, major AI conferences like NeurIPS, AAAI, and CVPR are setting new paper submission records every year. All this has led to complaints …

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