Image classification or computer vision is a branch of artificial intelligence where the task is to design systems that can recognise or classify objects based on digital images. It is a popular field due to the sheer breadth of applications -- image classification can be used for applications as diverse as recognising a particular flower from a photograph or to classifying medical images as normal or disease.
Roche (SIX: RO, ROG; OTCQX: RHHBY) today announced the research use only (RUO) launch of three new automated digital pathology algorithms, uPath Ki-67 (30-9), uPath ER (SP1) and uPath PR (1E2) image analysis for breast cancer, which are important biomarkers for breast cancer patients. Breast cancer is the second most common cancer in the world with an estimated 2.3 million new cases in 2020¹ and is the most common cancer in women globally¹,². These new algorithms complete the Roche digital pathology breast panel of image analysis algorithms. This includes a whole slide analysis workflow with automated pre-computing of the slide image prior to pathologist assessment, and a clear visual overlay highlighting tumour cells with and without nuclear staining. Intended for use with Roche's high medical value assays and slides stained on a BenchMark ULTRA instrument using ultraView DAB detection kit, the uPath Ki-67 (30-9) image analysis, uPath ER (SP1) image analysis and uPath PR (1E2) image analysis algorithms are ready-to-use and integrated within Roche's uPath enterprise software and NAVIFY Digital Pathology, the cloud version of uPath.
A new artificial intelligence (AI) system developed by researchers at the University of Waterloo and DarwinAI, an alumni-founded startup company, could help doctors efficiently utilize limited resources during the COVID-19 pandemic. The system is able to identify patients who require intensive care unit (ICU) treatment. The AI system predicts this necessity of ICU admission through the use of 200 clinical data points, which include blood test results, medical history, and vital signs. Alexander Wong is a professor of systems design engineering and Canada Research Chair in AI and Medical Imaging at Waterloo. "That is a very important step in the clinical decision support process for triaging patients and developing treatment plans," Wong said.
Freshness provides one of the essential characteristics for consumers. Consumers prefer fresh fruits rather than rotten ones when it comes to hygiene. An efficient fruit detection system is required to facilitate humans. So, for the easiness of people, this desktop application is proposed, named "Detection of Rotten Fruits (DRF)" by using Artificial Intelligence and Computer Vision. DRF is a desktop application for detecting rottenness in fruits that can be used to indicate the fruits according to their rottenness.
While it rarely happens for me, I occasionally observe something that is an actual watershed moment for technological advancement and can acknowledge that the possibility for good using said technologies might outweigh the potential for evil. Streaming your favorite shows has never been easier. For me, such a moment was watching The Beatles: Get Back, which is now airing on Disney . I had heard about it and seen some of its footage slowly being released for about a year, but I had no idea just how much technology had been applied to its production. I've seen many films made from historical footage from the 1960s and even the 1970 and 1980s, and so much of the source material is in poor condition.
The New South Wales government has teamed up with Cisco to trial the use of AI, IoT, and edge computing technology to improve the reliability of public transport in Sydney and Newcastle. As part of the trial, Transport for NSW (TfNSW) is using IoT to enable physical objects to be "digitised" and connected to the transported network via sensors, while edge computing will be leveraged to take real-time data from connected objects to enable faster decision making. AI, meanwhile, will be used to assist with understanding data and automating the process. The technologies will be connected to several buses, ferries, and light rail vehicles in both cities, the state government said. "We've partnered with Cisco to investigate how a real-time view of vehicle supply and customer demand, and performance, can guide future network decisions, and monitor road conditions to identify where repair work is needed," Minister for Transport and Roads Rob Stokes said.
For radiologists working today, the specter of artificial intelligence is inescapable. In the past year, venture capitalists have continued to invest significantly in startups developing AI for medical image analysis and support, with some groups projecting a $20 billion market by 2031. But despite the proliferation of research and investment, AI products are still a hard sell for many radiology practices -- even those at academic centers leading clinical research into deep learning. The tools are still in the middle of the hype cycle for any new technology: Inflated expectations are giving way to skepticism and barriers to adoption. Unlock this article by subscribing to STAT and enjoy your first 30 days free! STAT is STAT's premium subscription service for in-depth biotech, pharma, policy, and life science coverage and analysis.
To learn how to OCR a passport using OpenCV and Tesseract, just keep reading. So far in this course, we've relied on the Tesseract OCR engine to detect the text in an input image. However, as we discovered in a previous tutorial, sometimes Tesseract needs a bit of help before we can actually OCR the text. This tutorial will explore this idea more, demonstrating that computer vision and image processing techniques can localize text regions in a complex input image. Once the text is localized, we can extract the text ROI from the input image and then OCR it using Tesseract.
The use of correct data structures for every data science project is fundamental. In most simple projects we can work with tabular data, sometimes if we are lucky enough the tabular data will be easy to clean, and easy to handle missing values, and our project is ready to ramp up as we import our beautiful and clean .csv However this not apply to Image Classification tasks with TensorFlow and Keras.