Results


@Radiology_AI

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To determine whether deep learning algorithms developed in a public competition could identify lung cancer on low-dose CT scans with a performance similar to that of radiologists. In this retrospective study, a dataset consisting of 300 patient scans was used for model assessment; 150 patient scans were from the competition set and 150 were from an independent dataset. Both test datasets contained 50 cancer-positive scans and 100 cancer-negative scans. The reference standard was set by histopathologic examination for cancer-positive scans and imaging follow-up for at least 2 years for cancer-negative scans. The test datasets were applied to the three top-performing algorithms from the Kaggle Data Science Bowl 2017 public competition: grt123, Julian de Wit and Daniel Hammack (JWDH), and Aidence.


Retinal Vessel Segmentation Algorithm based on Residual Convolution Neural Network

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Retinal vessels are the only deep micro vessels that can be observed in human body, the accurate identification of which has great significance on the diagnosis of hypertension, diabetes and other diseases. To this end, a retinal vessel segmentation algorithm based on residual convolution neural network is proposed according to the characteristics of the retinal blood vessels on fundus images. Improved residual attention module and deep supervision module are utilized, in which the low-level and high-level feature graphs are joined to construct the encoder-decoder network structure, and atrous convolution is introduced to the pyramid pooling. The experiments result on the fundus image data set DRIVE and STARE show that this algorithm can obtain complete retinal vessel segmentation as well as connected vessel stems and terminals. The average accuracy on DRIVE and STARE reaches 95.90% and 96.88%, and the average specificity is 98.85% and 97.85%, which shows superior performance compared to other methods. This algorithm is verified feasible and effective for retinal vessel segmentation of fundus images and has the ability to detect more capillaries.


Automated deep learning-based paradigm for high-risk plaque detection in B-mode common carotid ultrasound scans: an asymptomatic Japanese cohort study

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BACKGROUND: The death due to stroke is caused by embolism of the arteries which is due to the rupture of the atherosclerotic lesions in carotid arteries. The lesion formation is over time, and thus, early screening is recommended for asymptomatic and moderate-risk patients. The previous techniques adopted conventional methods or semi-automated and, more recently, machine learning solutions. A handful of studies have emerged based on solo deep learning (SDL) models such as UNet architecture. METHODS: The proposed research is the first to adopt hybrid deep learning (HDL) artificial intelligence models such as SegNet-UNet.


Deep learning for disease diagnosis confounded by image labels – Physics World

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Artificial intelligence (AI) has potential to play a pivotal role in many areas of medicine. In particular, the use of deep learning to analyse medical images and improve the accuracy of disease diagnosis is a rapidly growing area of interest. But AI is not perfect. A new study has revealed that radiograph labels can confuse AI networks and limit their clinical utility. The problem arises due to a phenomenon called hidden stratification, in which convolutional neural networks (CNN) trained to analyse medical images learn to classify the image based on diagnostically irrelevant features.


Heartbeat Anomaly Detection

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According to a report of WHO, around 17.9 million people die each year due to Cardiovascular Diseases.Over the years it has been found that these deaths can be prevented if the diseases are diagnosed at an early stage and even the disease can be cured. Artificial Intelligence has been applied in various fields and one of them is AI for healthcare.We have seen AI practitioners coming up with solution for various disease diagnosis such as Cancer Detection, Detection of Diabetic Retinopathy and much more.The techniques used in these detections mostly involve Deep Learning. So, by combining our knowledge of deep learning and with its integration Iot we can develop a smart digital-stethoscope which can help in diagnosing anomalies in heartbeat in real-time and can help in classifying Cardio-diseases. While working in cAInvas one of its key features is UseCases Gallary.When working on any of its UseCases you don't have to look for data manually.As they have the feature to import your dataset to your workspace when you work on them.To load the data we just have to enter the following commands: As with all unstructured data formats, audio data has a couple of preprocessing steps which have to be followed before it is presented for analysis. Another way of representing audio data is by converting it into a different domain of data representation, namely the frequency domain.


Future Vision & Direction of AI Part II: Scaling AI Whilst Preventing a Big Brother World & Solving The Curse of the Modern Data Scientist

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Venture Capitalists are hoping to find the next superstar tech unicorn, AI startup founders dreaming of creating the next unicorn, and corporates adopting AI need to consider their data growth strategy in order to be able to scale their AI-enabled services or products. The past decade has been one of explosive growth in digital data and AI capabilities across the digital media and e-commerce space. And it is no accident that the strongest AI capabilities reside in the Tech majors. The author argues that there will be no AI winter in the 2020s as there was in 1974 and 1987 as the internet (social media and e-commerce) are so dependent upon AI capabilities and so too with being the Metaverse, and the era of 5G enabled Edge Computing with the Internet of Things (IoT). Furthermore, the following infographics illustrate how many people globally use social media and hence how central these channels have become to the everyday lives of people. Likewise, the size of the e-commerce market is vast. Although the era of standalone 5G networks may enable a window of opportunity for a new wave of consumer-facing applications in the business to consumer (B2C) in relation to e-commerce and perhaps even new digital media platforms that may challenge the current incumbents, after all the arrival of 4G provided a window for the likes of Airbnb, Uber, and leading social media platforms such as Facebook, Instagram, etc. to scale.


Artificial intelligence powers protein-folding predictions

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Rarely does scientific software spark such sensational headlines. "One of biology's biggest mysteries'largely solved' by AI", declared the BBC. Forbes called it "the most important achievement in AI -- ever". The buzz over the November 2020 debut of AlphaFold2, Google DeepMind's artificial-intelligence (AI) system for predicting the 3D structure of proteins, has only intensified since the tool was made freely available in July. The excitement relates to the software's potential to solve one of biology's thorniest problems -- predicting the functional, folded structure of a protein molecule from its linear amino-acid sequence, right down to the position of each atom in 3D space.


A Guide to Real World Artificial Intelligence & Machine Learning Use Cases

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Machine learning and artificial intelligence are driving major changes in the global economy. This article looks at the ways in which firms across the various sectors of the economy adopt Artificial Intelligence (AI) techniques. However, before we review the sectors affected it is important to note the underlying drivers that are fuelling the growth in the influence and reach of Machine Learning across the sectors of the economy will only grow as we move forwards. This is because Big Data is only getting larger, velocity of data faster, plus the availability of cheaper data storage plus the arrival of powerful Graphical Processing Units (GPUs) to enable Deep Learning algorithms to be deployed. Furthermore, new research in areas of Deep Learning and other Machine Learning areas will continue to emerge into real world production over the next few years leading to new opportunities and applications.


How AI Is Deepening Our Understanding of the Brain

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Artificial neural networks are famously inspired by their biological counterparts. Yet compared to human brains, these algorithms are highly simplified, even "cartoonish." Can they teach us anything about how the brain works? For a panel at the Society for Neuroscience annual meeting this month, the answer is yes. Deep learning wasn't meant to model the brain.


Deep learning reveals how proteins interact

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Scientist are now combining recent advances in evolutionary analysis and deep learning to build three-dimensional models of how most proteins in eukaryotes interact. The research effort has implications for understanding the biochemical processes that are common to all animals, plants, and fungi. The open-access work appears Nov. 11 in Science. As part of a multi-institutional collaboration, the lab of David Baker at the UW Medicine Institute for Protein Design helped guide this new development. "To really understand the cellular conditions that give rise to health and disease, it's essential to know how different proteins in a cell work together," Baker said.