AIMC Topic:
Image Interpretation, Computer-Assisted
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Graph Neural Networks for Gleason Grading in Prostate Histopathology Images.
Studies in health technology and informatics
May 15, 2025
Prostate cancer is a leading cause of cancer-related deaths, with Gleason grading being key for assessing tumor aggressiveness. We propose a Graph Neural Network-based approach to automate Gleason grading using the Automated Gleason Grading Challenge...
MIMI-ONET: Multi-Modal image augmentation via Butterfly Optimized neural network for Huntington DiseaseDetection.
Brain research
May 15, 2025
Huntington's disease (HD) is a chronic neurodegenerative ailment that affects cognitive decline, motor impairment, and psychiatric symptoms. However, the existing HD detection methods are struggle with limited annotated datasets that restricts their ...
Deep learning for cerebral vascular occlusion segmentation: A novel ConvNeXtV2 and GRN-integrated U-Net framework for diffusion-weighted imaging.
Neuroscience
May 14, 2025
Cerebral vascular occlusion is a serious condition that can lead to stroke and permanent neurological damage due to insufficient oxygen and nutrients reaching brain tissue. Early diagnosis and accurate segmentation are critical for effective treatmen...
An automated cascade framework for glioma prognosis via segmentation, multi-feature fusion and classification techniques.
Biomedical physics & engineering express
May 13, 2025
Glioma is one of the most lethal types of brain tumors, accounting for approximately 33% of all diagnosed brain tumor cases. Accurate segmentation and classification are crucial for precise glioma characterization, emphasizing early detection of mali...
Machine Learning and Deep Learning in Oncologic Imaging: Potential Hurdles, Opportunities for Improvement, and Solutions-Abdominal Imagers' Perspective.
Journal of computer assisted tomography
May 13, 2025
The applications of machine learning in clinical radiology practice and in particular oncologic imaging practice are steadily evolving. However, there are several potential hurdles for widespread implementation of machine learning in oncologic imagin...
Radiomics-Based Machine Learning Classification for Glioma Grading Using Diffusion- and Perfusion-Weighted Magnetic Resonance Imaging.
Journal of computer assisted tomography
May 13, 2025
OBJECTIVE: The aim of this study was to evaluate various radiomics-based machine learning classification models using the apparent diffusion coefficient (ADC) and cerebral blood flow (CBF) maps for differentiating between low-grade gliomas (LGGs) and...
Initial Experience With Low-Dose 18F-Fluorodeoxyglucose Positron Emission Tomography/Magnetic Resonance Imaging With Deep Learning Enhancement.
Journal of computer assisted tomography
May 13, 2025
OBJECTIVE: To demonstrate the utility of deep learning enhancement (DLE) to achieve diagnostic quality low-dose positron emission tomography (PET)/magnetic resonance (MR) imaging.
AI-Based Analysis of Abdominal Ultrasound Images to Support Medical Diagnosis in Emergency Departments.
Studies in health technology and informatics
May 2, 2025
The goal of segmentation in abdominal imaging for emergency medicine is to accurately identify and delineate organs, as well as to detect and localize pathological areas. This precision is critical for rapid, informed decision-making in acute care sc...
Feasibility of virtual T2-weighted fat-saturated breast MRI images by convolutional neural networks.
European radiology experimental
May 2, 2025
BACKGROUND: Breast magnetic resonance imaging (MRI) protocols often include T2-weighted fat-saturated (T2w-FS) sequences, which support tissue characterization but significantly increase scan time. This study aims to evaluate whether a 2D-U-Net neura...
Interactive Explainable Deep Learning Model for Hepatocellular Carcinoma Diagnosis at Gadoxetic Acid-enhanced MRI: A Retrospective, Multicenter, Diagnostic Study.
Radiology. Imaging cancer
May 1, 2025
Purpose To develop an artificial intelligence (AI) model based on gadoxetic acid-enhanced MRI to assist radiologists in hepatocellular carcinoma (HCC) diagnosis. Materials and Methods This retrospective study included patients with focal liver lesion...
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