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Artificial intelligence

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Deep learning[133] uses several layers of neurons between the network's inputs and outputs. The multiple layers can progressively extract higher-level features from the raw input. For example, in image processing, lower layers may identify edges, while higher layers may identify the concepts relevant to a human such as digits or letters or faces.[134] Deep learning has drastically improved the performance of programs in many important subfields of artificial intelligence, including computer vision, speech recognition, image classification[135] and others. Deep learning often uses convolutional neural networks for many or all of its layers.


A new generative adversarial network for medical images super resolution - Scientific Reports

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For medical image analysis, there is always an immense need for rich details in an image. Typically, the diagnosis will be served best if the fine details in the image are retained and the image is available in high resolution. In medical imaging, acquiring high-resolution images is challenging and costly as it requires sophisticated and expensive instruments, trained human resources, and often causes operation delays. Deep learning based super resolution techniques can help us to extract rich details from a low-resolution image acquired using the existing devices. In this paper, we propose a new Generative Adversarial Network (GAN) based architecture for medical images, which maps low-resolution medical images to high-resolution images. The proposed architecture is divided into three steps. In the first step, we use a multi-path architecture to extract shallow features on multiple scales instead of single scale. In the second step, we use a ResNet34 architecture to extract deep features and upscale the features map by a factor of two. In the third step, we extract features of the upscaled version of the image using a residual connection-based mini-CNN and again upscale the feature map by a factor of two. The progressive upscaling overcomes the limitation for previous methods in generating true colors. Finally, we use a reconstruction convolutional layer to map back the upscaled features to a high-resolution image. Our addition of an extra loss term helps in overcoming large errors, thus, generating more realistic and smooth images. We evaluate the proposed architecture on four different medical image modalities: (1) the DRIVE and STARE datasets of retinal fundoscopy images, (2) the BraTS dataset of brain MRI, (3) the ISIC skin cancer dataset of dermoscopy images, and (4) the CAMUS dataset of cardiac ultrasound images. The proposed architecture achieves superior accuracy compared to other state-of-the-art super-resolution architectures.


Disease Classification using Medical MNIST

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The objective of this study is to classify medical images using the Convolutional Neural Network(CNN) Model. Here, I trained a CNN model with a well-processed dataset of medical images. This model can be used to classify medical images based on categories provided as per the training dataset. This dataset was developed in 2017 by Arturo Polanco Lozano. It is also known as the MedNIST dataset for radiology and medical imaging. For the preparation of this dataset, images have been gathered from several datasets, namely, TCIA, the RSNA Bone Age Challange, and the NIH Chest X-ray dataset.


Artificial intelligence predicts patients' race from their medical images

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The miseducation of algorithms is a critical problem; when artificial intelligence mirrors unconscious thoughts, racism, and biases of the humans who generated these algorithms, it can lead to serious harm. Computer programs, for example, have wrongly flagged Black defendants as twice as likely to reoffend as someone who's white. When an AI used cost as a proxy for health needs, it falsely named Black patients as healthier than equally sick white ones, as less money was spent on them. Even AI used to write a play relied on using harmful stereotypes for casting. Removing sensitive features from the data seems like a viable tweak.


Doctors Are Very Worried About Medical AI That Predicts Race

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To conclude, our study showed that medical AI systems can easily learn to recognise self-reported racial identity from medical images, and that this capability is extremely difficult to isolate,


Image Classification in Machine Learning [Intro + Tutorial]

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Image Classification is one of the most fundamental tasks in computer vision. It has revolutionized and propelled technological advancements in the most prominent fields, including the automobile industry, healthcare, manufacturing, and more. How does Image Classification work, and what are its benefits and limitations? Keep reading, and in the next few minutes, you'll learn the following: Image Classification (often referred to as Image Recognition) is the task of associating one (single-label classification) or more (multi-label classification) labels to a given image. Here's how it looks like in practice when classifying different birds-- images are tagged using V7. Image Classification is a solid task to benchmark modern architectures and methodologies in the domain of computer vision. Now let's briefly discuss two types of Image Classification, depending on the complexity of the classification task at hand. Single-label classification is the most common classification task in supervised Image Classification.


10 Best AI Courses: Beginner to Advanced

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Are you looking for the Best Certification Courses for Artificial Intelligence?. If yes, then your search will end after reading this article. In this article, I will discuss the 10 Best Certification Courses for Artificial Intelligence. So, give your few minutes to this article and find out the Best AI Certification Course for you. Artificial Intelligence is changing our lives.


Analytical Scientist in Digital Pathology and Tissue-based Artificial Intelligence (ID220) in Sutton (Greater London)

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Candidates must have a PhD (or equivalent) in computer science or other related quantitative subject, with demonstrable knowledge in programming and image analysis. Ideally, the successful candidates would have experience in tissue computational analysis, and in the development of new tools and pipeline for accurate image analysis and biomarker quantitation. They must be proficient with modern high-level programming languages like Python, R, Java and have experience in image processing tools and libraries (OpenCV, scikit0image, ImageJ). Experience in Deep Learning frameworks is desirable.


A New Artificial Intelligence Model Has Been Developed to Detect Covid–19 Disease From Cough Sound

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While scientists continue their fight against SARS–CoV–2, one of the deadliest viruses in the last ten years, with antigen diagnostic tests, tests that help diagnose and prognosis, drugs, and vaccine inventions, informatics mostly continue to work on early detection diagnosis, prognosis, and prediction in this period. The aim is to reveal systems with a low margin of error that can help the workload of healthcare professionals and early diagnosis and initiation of treatment. The most commonly used computer vision (automation of the processes of vision and perception in humans, high–level interpretation on digital images or videos on the computer) is the processing of radiological images. Automation of the same image interpretation process for the performance of many applications and imaging results can be easily accomplished with complex and powerful computational platforms of large–scale data such as Deep Learning. With deep understanding, the manual design of these features is eliminated, and a large amount of different classification and regression tasks are completed with higher accuracy.


Top 108 Computer Vision startups

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Computer vision is an interdisciplinary field that deals with how computers can be made for gaining high-level understanding from digital images or videos. From the perspective of engineering, it seeks to automate tasks that the human visual system can do. Country: China Funding: $1.6B SenseTime develops face recognition technology that can be applied to payment and picture analysis, which could be used, for instance, on bank card verification and security systems. Country: China Funding: $607M Megvii develops Face Cognitive Services - a platform offering computer vision technologies that enable your applications to read and understand the world better. Face allows you to easily add leading, deep learning-based image analysis recognition technologies into your applications, with simple and powerful APIs and SDKs.