Visiopharm A/S announces the first result of their multifaceted strategy to apply Deep Learning technologies to its leading image analysis solution for cancer research and diagnostics. Visiopharm considers Deep Learning an important technological breakthrough for tissue pathology that offers the potential to make a real difference in the assessment of tissue structures, which is probably one of…
Royal Philips (NYSE: PHG, AEX: PHIA) and LabPON, the first clinical laboratory to transition to 100% histopathology digital diagnosis, today announced its plans to create a digital database of massive aggregated sets of annotated pathology images and big data utilizing Philips IntelliSite Pathology Solution1. The database will provide pathologists with a wealth of clinical information for the…
Autonomous driving, automatic speech recognition, and the game Go: Deep Learning is generating more and more public awareness. Scientists at the Helmholtz Zentrum München and their partners at ETH Zurich and the Technical University of Munich (TUM) have now used it to determine the development of hematopoietic stem cells in advance. In ‘Nature Methods’ they describe how their software…
Physicians have long used visual judgment of medical images to determine the course of cancer treatment. A new program package from Fraunhofer researchers reveals changes in images and facilitates this task using deep learning. The experts will demonstrate this software in Chicago from November 27 to December 2 at RSNA, the world’s largest radiology meeting.
Surgery will change – with all the challenges that developments such as Big Data create there are no two ways about it. However, how deep those changes run remains to be seen. In a rather young field of research, scientists look at the ways all components used during surgery can be interlinked. Professor Beat Müller, co-initiator of the project ‘Cognition-Guided Surgery’, explains results…
Mechanical ventilation and subspecialisation are key aspects of modern anaesthesia and critical care practice according to Dr Javier Garcia Fernandez, Head of Critical Care and Anaesthesiology at University Hospital Puerta de Hierro in Madrid.
Pathologists have been largely diagnosing disease the same way for the past 100 years, by manually reviewing images under a microscope. But new work suggests that computers can help doctors improve accuracy and significantly change the way cancer and other diseases are diagnosed. A research team from Beth Israel Deaconess Medical Center (BIDMC) and Harvard Medical School (HMS) recently developed…
Big data has the potential to offer a better understanding of how to aggregate clinically relevant data on a large scale and deliver better computer aided diagnosis algorithms and tools.
MRI, CT, pathology: doctors have to consider medical image data – increasing in both amount and complexity – to perform diagnoses and monitor therapy. The Fraunhofer Institute for Medical Image Computing MEVIS in Bremen is creating a new approach to provide effective assistance.
‘In IT we often casually say that Big Data is exactly what we can’t do yet,’ said Professor Christoph Meinel, President of Germany’s Hasso-Plattner-Institute, ruefully. We asked the computer science expert about the potential of big data in medicine and medical research.
With the help of artificial intelligence, computers are to simulate human thought processes. Machine learning is intended to support almost all medical specialties. But what is going on inside an AI algorithm, what are its decisions based on? Can you even entrust a medical diagnosis to a machine? Clarifying these questions remains a central aspect of AI research and development.