The purpose of this study was to accelerate MR cholangiopancreatography (MRCP) acquisitions using deep learning-based (DL) reconstruction at 3 and 0.55 T. A total of 35 healthy volunteers underwent conventional twofold accelerated MRCP scans at field...
STUDY QUESTION: How can we best achieve tissue segmentation and cell counting of multichannel-stained endometriosis sections to understand tissue composition?
Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi
Feb 25, 2025
Due to its irregular shape and varying contour, pancreas segmentation is a recognized challenge in medical image segmentation. Convolutional neural network (CNN) and Transformer-based networks perform well but have limitations: CNN have constrained r...
Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi
Feb 25, 2025
In the clinical stage, suspected hemolytic plasma may cause hemolysis illness, manifesting as symptoms such as heart failure, severe anemia, etc. Applying a deep learning method to plasma images significantly improves recognition accuracy, so that th...
Mathematical biosciences and engineering : MBE
Feb 20, 2025
The automated detection of tumors using medical imaging data has garnered significant attention over the past decade due to the critical need for early and accurate diagnoses. This interest is fueled by advancements in computationally efficient model...
BACKGROUND: Automatic landmarking software packages simplify the analysis of the 3D facial images. Their main deficiency is the limited accuracy of detecting landmarks for routine clinical applications. Cliniface is readily available open-access soft...
MOTIVATION: The classification task based on whole-slide images (WSIs) is a classic problem in computational pathology. Multiple instance learning (MIL) provides a robust framework for analyzing whole slide images with slide-level labels at gigapixel...
Translational vision science & technology
Feb 3, 2025
PURPOSE: This study aims to develop an automated pipeline to detect retinal detachment from B-scan ocular ultrasonography (USG) images by using deep learning-based segmentation.
PURPOSE: This study presents a novel application of deep learning to enhance the accuracy of left-right orientation identification in anatomical brain MRI scans. Left-right orientation misidentification in brain MRIs presents significant challenges d...
The international journal of medical robotics + computer assisted surgery : MRCAS
Feb 1, 2025
BACKGROUND: This research aims to use deep learning to create automated systems for better breast cancer detection and categorisation in mammogram images, helping medical professionals overcome challenges such as time consumption, feature extraction ...
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