BACKGROUND: The daVinci Single-Port (SP) robot is a new robotic platform designed to overcome the challenges of Single-Incision Laparoscopic Surgery. The objective of this study is to demonstrate the feasibility and technical aspects of SP robotic (S...
Artificial intelligence (AI) can extract complex information from visual data. Histopathology images of gastrointestinal (GI) and liver cancer contain a very high amount of information which human observers can only partially make sense of. Complemen...
Machine learning offers great opportunities to streamline and improve clinical care from the perspective of cardiac imagers, patients, and the industry and is a very active scientific research field. In light of these advances, the European Society o...
European journal of nuclear medicine and molecular imaging
Nov 19, 2020
Systematic imaging can be broadly defined as the systematic identification and characterization of biological processes at multiple scales and levels. In contrast to "classical" diagnostic imaging, systematic imaging emphasizes on detecting the overa...
RoFo : Fortschritte auf dem Gebiete der Rontgenstrahlen und der Nuklearmedizin
Nov 19, 2020
PURPOSE: A recently developed deep learning model (U-Net) approximated the clinical performance of radiologists in the prediction of clinically significant prostate cancer (sPC) from prostate MRI. Here, we compare the agreement between lesion segmen...
Skin cancer is among the 10 most common cancers. Recent research revealed the superiority of artificial intelligence (AI) over dermatologists to diagnose skin cancer from predesignated and cropped images. However, there remain several uncertainties f...
International journal of medical informatics
Nov 19, 2020
BACKGROUND: Reliable localization and tracking of the eye region in the pediatric hospital environment is a significant challenge for clinical decision support and patient monitoring applications. Existing work in eye localization achieves high perfo...
Convolutional Neural Networks (CNNs) work very well for supervised learning problems when the training dataset is representative of the variations expected to be encountered at test time. In medical image segmentation, this premise is violated when t...
Two of the most common tasks in medical imaging are classification and segmentation. Either task requires labeled data annotated by experts, which is scarce and expensive to collect. Annotating data for segmentation is generally considered to be more...
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