Deep learning

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Survival prediction

Deep learning may lead to better lung cancer treatments

Doctors and healthcare workers may one day use a machine learning model, called deep learning, to guide their treatment decisions for lung cancer patients, according to a team of Penn State Great…

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Applications of machine learning

Training AI to predict outcomes for cancer patients

Predicting the outcome of cancer can help the clinical decision-making process related to a patient’s treatment. The potential for Artificial Intelligence (AI) to support this was a key facet of…

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AI use in clinical diagnosis

Deep learning tool predicts tumour expression from whole slide images

A deep learning model to predict RNA-Seq expression of tumours from whole slide images was among the industry innovations outlined at the 7th Digital Pathology and AI Congress for Europe. Created by…

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AI does not discriminate

Removing bias from mammography screening with deep learning

AI can help tackle inequities and bias in healthcare but it also brings partiality issues of its own, experts explained in a Hot Topic session entitled "Artificial Intelligence and Implications…

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Clinical decision support

AI deep learning of PET/CT images to support NSCLC treatment

A software tool to predict the most effective therapy for non-small cell lung cancer (NSCLC) developed by applying deep learning artificial intelligence (AI) to positron emission tomography/computed…

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AI-assisted MRI segmentation

Deep learning boost for prostate cancer workflow

Prostate cancer radiotherapy treatments guided by MRI are increasingly being explored to help improve patient outcomes and reduce toxicities after treatment. However, this development is being held…

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Algorithms must meet quality criteria

Deep Learning in breast cancer detection

A French expert in breast imaging looked at the latest Deep Learning (DL) applications in her field, screening their strengths and weaknesses in improving breast cancer detection. It is really important to understand which types of data sets need to be checked when evaluating an AI model for image interpretation, according to Isabelle Thomassin-Naggara, Professor of Radiology at Sorbonne…

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Improving the role of radiology

Value-based healthcare: AI reveals the bigger picture

Value-based healthcare is gaining momentum and radiologists must increasingly show their contribution in improving patient care. Artificial intelligence (AI) can help them to do so and brings a series of new opportunities, according to Charles E Kahn, Professor and Vice Chairman of Radiology at the University of Pennsylvania, speaking at a meeting in Madrid in January. AI can do a lot to improve…

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Deep learning application

COVID-19 cough camera: device detects location of coughing sounds in real-time​

The Center for Noise and Vibration Control at the Korea Advanced Institute of Science and Technology (KAIST) announced that their coughing detection camera recognizes where coughing happens, visualizing the locations. The resulting cough recognition camera can track and record information about the person who coughed, their location, and the number of coughs on a real-time basis. Professor…

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Brain tumor treatment network

'Federated learning' AI approach allows hospitals to share patient data privately

To answer medical questions that can be applied to a wide patient population, machine learning models rely on large, diverse datasets from a variety of institutions. However, health systems and hospitals are often resistant to sharing patient data, due to legal, privacy, and cultural challenges. An emerging technique called federated learning is a solution to this dilemma, according to a study…

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Deep learning in imaging

1.5T MR system receives FDA clearance for AI-based image reconstruction technology

Canon Medical Systems USA, Inc. has received 510(k) clearance on its Advanced intelligent Clear-IQ Engine (AiCE) for the Vantage Orian 1.5T MR system, continuing to expand access to its new Deep Learning Reconstruction (DLR) technology. This technology, which is also available on the Vantage Galan 3T MR system and across a majority of Canon Medical’s CT product portfolio, uses a deep learning…

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Deep Learning in Radiology

New Levels of Precision with Self-learning Imaging Software

The complex form of machine learning DLIR (Deep Learning Image Reconstruction) is based on a deep neuronal network which is similar to the human brain. The artificial neurons of this network learn according to their biological model through intensive training. For the DLIR image reconstruction, the network is fed with sample data from phantom images on the one hand and high-resolution images of…

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Expectations vs. reality

AI in clinical practice: how far we are and how we can go further

Luis Martí-Bonmatí, Director of the Medical Imaging Department at La Fe Hospital in Valencia, highlighted the need to assess utility when developing AI tools during ECR 2020. Artificial intelligence (AI) can impact and improve many aspects of clinical practice. But current expectations are too great and need to be toned down by looking at opportunities.

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Digital pathology

VIPR: Deep learning for small cohorts

To investigate rare diseases, applying image-based analytics approaches, including the use of deep learning convolutional neural networks (DL-CNNs), can be a major challenge due to great difficulties in acquiring sufficient numbers of cases and associated digital image sets from the small cohorts typically available. To realise algorithms that are both effective and generalisable, conventional…

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Sample analysis

Next-generation analytical lab software strengthens data exploration

Scientists in the life sciences can now benefit from upgrades to a suite of analytical software solutions with new features designed to enhance productivity, confidence and accuracy in numerous fields, including proteomics, food safety and biotherapeutic drug development. The latest suite of software strengthens laboratory workflows across a range of applications through expanded capabilities,…

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From physical to computational staining

Deep learning accurately stains digital biopsy H&E slides

Tissue biopsy slides stained using hematoxylin and eosin (H&E) dyes are a cornerstone of histopathology, especially for pathologists needing to diagnose and determine the stage of cancers. A research team led by MIT scientists at the Media Lab, in collaboration with clinicians at Stanford University School of Medicine and Harvard Medical School, now shows that digital scans of these biopsy…

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Coronavirus imaging

AI enhanced lung ultrasound for COVID-19 testing

Establishing whether a patient is suffering from severe lung disease, possibly COVID-19, within a few minutes: this is possible using fairly simple ultrasound machines that are enhanced with artificial intelligence. A research team at Eindhoven University of Technology (TU/e) and the University of Trento in Italy has been able to translate the expertise of top lung specialists into a software…

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Algorithm-assisted diagnostics

AI in imaging: not as reliable as you'd think

Machine learning and AI are highly unstable in medical image reconstruction, and may lead to false positives and false negatives, a new study suggests. A team of researchers, led by the University of Cambridge and Simon Fraser University, designed a series of tests for medical image reconstruction algorithms based on AI and deep learning, and found that these techniques result in myriad…

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Tools for practitioners

Computational pathology: Heading for personalised medicine

Computational pathology has increased applications for diagnosis, prediction of prognosis and therapy response, facilitating the movement of healthcare towards personalised medicine. Coupled with deep learning, such tools are ever more efficient and robust within research and clinical settings. The growing role of computational pathology was highlighted by Professor Andrew Janowczyk at the…

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Experts express doubts

AI outperforming doctors: hype, exaggeration or fact?

Many studies claiming that artificial intelligence (AI) is as good as (or better than) human experts at interpreting medical images are of poor quality and are arguably exaggerated, posing a risk for the safety of ‘millions of patients’ warn researchers in The BMJ. Their findings raise concerns about the quality of evidence underpinning many of these studies, and highlight the need to improve…

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Deep learning & CNN

Algorithm differentiates small renal masses on multiphase CT

A deep learning method with a convolutional neural network (CNN) can support the evaluation of small solid renal masses in dynamic CT images with acceptable diagnostic performance, according to an article published ahead-of-print in the March issue of the American Journal of Roentgenology (AJR). Between 2012 and 2016, researchers at Japan’s Okayama University studied 1807 image sets from 168…

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