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Automatic medical report generation based on deep learning: A state of the art survey.
Comput Med Imaging Graph
January 2025
College of Medicine and Biological Information Engineering, Northeastern University, 110819, China. Electronic address:
With the increasing popularity of medical imaging and its expanding applications, posing significant challenges for radiologists. Radiologists need to spend substantial time and effort to review images and manually writing reports every day. To address these challenges and speed up the process of patient care, researchers have employed deep learning methods to automatically generate medical reports.
View Article and Find Full Text PDFBiomarkers.
Alzheimers Dement
December 2024
Konkuk University School of Medicine, Seoul, Korea, Republic of (South).
Background: Choroid plexus enlargement is implicated in the exacerbation of cognitive impairments characteristic of Alzheimer's dementia (AD). Manual volumetric assessment by radiologists, while accurate, is impractical for large-scale application due to its resource-intensive nature. We examine the use of automated brain volumetry software as an efficient and cost-effective alternative for quantifying choroid plexus volume.
View Article and Find Full Text PDFAlzheimer's Imaging Consortium.
Alzheimers Dement
December 2024
Konkuk University School of Medicine, Seoul, Korea, Republic of (South).
Background: Choroid plexus enlargement is implicated in the exacerbation of cognitive impairments characteristic of Alzheimer's dementia (AD). Manual volumetric assessment by radiologists, while accurate, is impractical for large-scale application due to its resource-intensive nature. We examine the use of automated brain volumetry software as an efficient and cost-effective alternative for quantifying choroid plexus volume.
View Article and Find Full Text PDFComputer-Aided Detection (CADe) and Segmentation Methods for Breast Cancer Using Magnetic Resonance Imaging (MRI).
J Magn Reson Imaging
January 2025
Advanced Diagnostic and Interventional Radiology Research Center, Tehran University of Medical Sciences, Tehran, Iran.
Breast cancer continues to be a major health concern, and early detection is vital for enhancing survival rates. Magnetic resonance imaging (MRI) is a key tool due to its substantial sensitivity for invasive breast cancers. Computer-aided detection (CADe) systems enhance the effectiveness of MRI by identifying potential lesions, aiding radiologists in focusing on areas of interest, extracting quantitative features, and integrating with computer-aided diagnosis (CADx) pipelines.
View Article and Find Full Text PDFFaster Acquisition and Improved Image Quality of T2-Weighted Dixon Breast MRI at 3T Using Deep Learning: A Prospective Study.
Korean J Radiol
January 2025
Department of Diagnostic and Interventional Radiology, University Medical Center Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany.
Objective: The aim of this study was to compare image quality features and lesion characteristics between a faster deep learning (DL) reconstructed T2-weighted (T2-w) fast spin-echo (FSE) Dixon sequence with super-resolution (T2) and a conventional T2-w FSE Dixon sequence (T2) for breast magnetic resonance imaging (MRI).
Materials And Methods: This prospective study was conducted between November 2022 and April 2023 using a 3T scanner. Both T2 and T2 sequences were acquired for each patient.
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