Deep learning 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. Deep learning has drastically improved the performance of programs in many important subfields of artificial intelligence, including computer vision, speech recognition, image classification and others. Deep learning often uses convolutional neural networks for many or all of its layers.
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.
Though marketers are still in the early stages of experimenting with deepfakes and deepfake technology, these videos convey a more immersive marketing experience through storytelling. Deepfake technology is a type of "deep learning." Deep learning is a machine learning type that allows computers to learn tasks independently without being explicitly programmed. Deepfake technology also involves computer vision, allowing computers to recognize objects in images. For example, computer vision uses deep learning algorithms to identify objects in photos or videos.
What you're seeing in the image above (middle image, man in blue shirt), as well as the image directly below (left image, man in blue shirt), is not a'real' video into which a small patch of'fake' face has been superimposed, but an entirely synthesized scene that exists solely as a volumetric neural rendering – including the body and background: In the example directly above, the real-life video on the right (woman in red dress) is used to'puppet' the captured identity (man in blue shirt) on the left via RigNeRF, which (the authors claim) is the first NeRF-based system to achieve separation of pose and expression while being able to perform novel view syntheses. The male figure on the left in the image above was'captured' from a 70-second smartphone video, and the input data (including the entire scene information) subsequently trained across 4 V100 GPUs to obtain the scene. Since 3DMM-style parametric rigs are also available as entire-body parametric CGI proxies (rather than just face rigs), RigNeRF potentially opens up the possibility of full-body deepfakes where real human movement, texture and expression is passed to the CGI-based parametric layer, which would then translate action and expression into rendered NeRF environments and videos. As for RigNeRF – does it qualify as a deepfake method in the current sense that the headlines understand the term? Or is it just another semi-hobbled also-ran to DeepFaceLab and other labor-intensive, 2017-era autoencoder deepfake systems?
Research¹ shows that including videos in web pages can effectively improve user experiences, increase Search Engine Optimization (SEO), and catch readers further down the sales funnel. To help agents with their business through Compass' website, the Compass AI Content Intelligence (AI-CI) team wants to make it easy for them to generate and share videos. We leverage state-of-the-art AI technologies to create visual and textual content for the videos to be generated and leverage the close to metal rendering algorithms together with the cloud-based distributed computation system to render the videos efficiently. With our current automatic video generation feature, agents can create a video with a single click, or with just a few more clicks they can customize it. They can then quickly review videos that have been created for them.
Originally published on Towards AI the World's Leading AI and Technology News and Media Company. If you are building an AI-related product or service, we invite you to consider becoming an AI sponsor. At Towards AI, we help scale AI and technology startups. Let us help you unleash your technology to the masses. As we go into June, the AI world doesn't stop and once again the pace of new stories and research was high. The ACL conference was held in the past month in Dublin, being one of the first major conferences to go back in person, which certainly feels like another step forward into normalcy.
ScImage Inc., a leading provider of Enterprise Imaging solutions and DiA Imaging Analysis, a global leading provider of AI-based cardiac ultrasound software, announced a commercial partnership to combine ScImage's unique Cloud architecture with DiA's AI-based automated cardiac ultrasound solution, LVivo Seamless. The collaboration leverages each company's strengths to give echocardiography (echo) labs greater access to the latest innovations in healthcare imaging technology. ScImage's intelligent Cloud computing infrastructure together with DiA's AI-based algorithms, will now be available to more echocardiologists and other imaging specialists, enabling them to maximize workflow efficiency in the echo lab environment and improve patient care. "ScImage prides itself on delivering the most progressive, secure, True Cloud offering in healthcare today. By combining the compute power of PICOM365 with DiA's LVivo Seamless, clinicians will be able to enjoy the highest level of quantitative image analysis and longitudinal measurement accuracy," said Sai Raya, Ph.D., ScImage's Founder and CEO.
The Terminator movie series made it clear that robots would take over our world one day and the massive emergence of technology is bringing about a disruption in the media and entertainment industry. Today we are going to take a look at Deepfake technology and how it is imparting a facelift to the media ecosystem. Deepfake is an AI-based media synthesizing technique that includes manipulating sounds and superimposing human features on another person's face/body to render a real human experience. Deep learning technology is used by the Deepfake app to imitate a person's actions, looks, and mannerisms without requiring them to be present. This results in hyper-realistic audio and video which is impossible to distinguish from the real thing.
A deepfake video created by Dutch police could help to change the often negative perception of the technology. Deepfakes use generative neural network architectures – such as autoencoders or generative adversarial networks (GANs) – to manipulate or generate visual and audio content. The technology is already being used for malicious purposes including generating sexual content of individuals without their consent, fraud, and the creation of deceptive content aimed at changing views and influencing democratic processes. However, authorities in Rotterdam have proven the technology can be put to use for good. Dutch police have created a deepfake video of 13-year-old Sedar Soares – a young footballer who was shot dead in 2003 while throwing snowballs with his friends in the car park of a Rotterdam metro station – in an appeal for information to finally solve his murder.