Pulmonary opacification is the inflammation in the lungs caused by many respiratory ailments, including the novel corona virus disease 2019 (COVID-19). Chest X-rays (CXRs) with such opacifications render regions of lungs imperceptible, making it difficult to perform automated image analysis on them. In this work, we focus on segmenting lungs from such abnormal CXRs as part of a pipeline aimed at automated risk scoring of COVID-19 from CXRs. We treat the high opacity regions as missing data and present a modified CNN-based image segmentation network that utilizes a deep generative model for data imputation. We train this model on normal CXRs with extensive data augmentation and demonstrate the usefulness of this model to extend to cases with extreme abnormalities.

The second generation of Intel Xeon Scalable processors introduced a collection of features for deep learning, packaged together as Intel Deep Learning Boost. These features include Vector Neural Network Instructions (VNNI), which increases throughput for inference applications with support for INT8 convolutions by combining multiple machine instructions from previous generations into one machine instruction.

I've been playing around with OpenAI's new GPT-3 language model. When I got beta access, the first thing I wondered was, how human is GPT-3? How close is it to passing a Turing test? Let me explain how exactly I'm generating these conversations. GPT-3 is a general language model, trained on a large amount of uncategorized text from the internet.

Summary: New machine learning technology that researchers have made free online can be used by sleep experts to study the role of K-complex, a brief but prominent pattern of brain activity that lasts around half a second during sleep. Algorithms and deep learning has enabled Flinders University sleep researchers to dive deep into one of the mysteries of sleep health. They have used machine learning and artificial intelligence to develop a free online tool being used by sleep experts and researchers around the world to work out the role of the so-called K-complex, a prominent, brief up-down-up pattern of brain electro-encephalogram (EEG) electrical activity lasting around half a second during sleep. When displayed on an EEG screen, it looks a bit like a'K', says Bastien Lechat, lead author on a new Flinders University paper published in Sleep journal. "We hope this algorithm will help to fast forward new discoveries regarding the mysterious K-complex waveform and its associated health outcomes."

Image colorization is an engaging topic in the field of image-to-image translation. Even though color photography was invented in 1907, It didn't become popular for the average person until the 1960s because of its expensiveness and inaccessibility. All the photography and videography up until then was done on Black & White. Colorizing these images was impossible--until the DeOldify deep learning model came to life. DeOldify is an open-source deep learning model created by Jason Antic, used to add high-quality colorization to grayscale images with amazing results.

TOKYO (Reuters) - If you've ever bought supermarket sushi, you may know the taste trauma that hit Kazuhiro Shimura one night. But "disappointing" tuna sparked an idea: he'd develop an artificial intelligence (AI) system to make sure your sashimi is always delicious. Shimura, a director at advertising firm Dentsu Group's (4324.T) Future Creative Center, came up with the concept for "Tuna Scope" AI as he chewed his raw dish while watching a television show on fish merchants who spend a decade mastering the skill of selecting high-quality tuna for sushi restaurants. Using a deep learning algorithm to crunch through grading data from merchants, Tuna Scope has now evolved into a smartphone app. Clients can download and use it anywhere, creating "a unified grading standard" for an industry that relies on local know-how, said Shimura, who is working with Japanese trading company Sojitz Corp (2768.T) to promote his technology.

Since it was introduced by the Facebook AI Research (FAIR) team, back in early 2017, PyTorch has become a highly popular and widely used Deep Learning (DL) framework. Since the humble beginning, it has caught the attention of serious AI researchers and practitioners around the world, both in industry and academia, and has matured significantly over the years. Scores of DL enthusiasts and professionals started their journey with the Google TensorFlow (TF), but the learning curve with base TensorFlow has always been steep. On the other hand, PyTorch has approached DL programming in an intuitive fashion since the beginning, focusing on fundamental linear algebra and data flow operations in a manner that is easily understood and amenable to step-by-step learning. Due to this modular approach, building and experimenting with complex DL architectures has been much easier with PyTorch than following the somewhat rigid framework of TF and TF-based tools.

The Ki-67 index is an established prognostic factor in gastrointestinal neuroendocrine tumors (GI-NETs) and defines tumor grade. It is currently estimated by microscopically examining tumor tissue single-immunostained (SS) for Ki-67 and counting the number of Ki-67-positive and Ki-67-negative tumor cells within a subjectively picked hot-spot. Intraobserver variability in this procedure as well as difficulty in distinguishing tumor from non-tumor cells can lead to inaccurate Ki-67 indices and possibly incorrect tumor grades. We introduce two computational tools that utilize Ki-67 and synaptophysin double-immunostained (DS) slides to improve the accuracy of Ki-67 index quantitation in GI-NETs: (1) Synaptophysin-KI-Estimator (SKIE), a pipeline automating Ki-67 index quantitation via whole-slide image (WSI) analysis and (2) deep-SKIE, a deep learner-based approach where a Ki-67 index heatmap is generated throughout the tumor. Ki-67 indices for 50 GI-NETs were quantitated using SKIE and compared with DS slide assessments by three pathologists using a microscope and a fourth pathologist via manually ticking off each cell, the latter of which was deemed the gold standard (GS). Compared to the GS, SKIE achieved a grading accuracy of 90% and substantial agreement (linear-weighted Cohen’s kappa 0.62). Using DS WSIs, deep-SKIE displayed a training, validation, and testing accuracy of 98.4%, 90.9%, and 91.0%, respectively, significantly higher than using SS WSIs. Since DS slides are not standard clinical practice, we also integrated a cycle generative adversarial network into our pipeline to transform SS into DS WSIs. The proposed methods can improve accuracy and potentially save a significant amount of time if implemented into clinical practice.

Clustering: Any enterprise needs to concentrate on knowing clients: who are they, and what affects their buying decisions? Clustering algorithms will move through the data and will try to find such existing clusters. You can also increase the number of clusters in your algorithm which will help to you to adjust the granularity of these groups. Unsupervised Machine Learning: What is, Algorithms, Example and its Application. It's the year 2030, and you have a bustling day planned.

It seems everywhere you look nowadays, you will find an article that describes a winning strategy using deep learning in a data science problem, or more specifically in the field of artificial intelligence (AI). However, clear explanations of deep learning, why it's so powerful, and the various forms deep learning takes in practice, are not so easy to come by. In order to know more about deep learning, neural networks, the major innovations, the most widely used paradigms, where deep learning works and doesn't, and even a little of the history, we have asked and answered a few basic questions. Deep learning is the modern evolution of traditional neural networks. Indeed, to the classic feed-forward, fully connected, backpropagation trained, multilayer perceptrons (MLPs), "deeper" architectures have been added.