AIMC Topic: Image Interpretation, Computer-Assisted
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Enhancing classification of active and non-active lesions in multiple sclerosis: machine learning models and feature selection techniques.
BMC medical imaging
Dec 20, 2024
INTRODUCTION: Gadolinium-based T1-weighted MRI sequence is the gold standard for the detection of active multiple sclerosis (MS) lesions. The performance of machine learning (ML) and deep learning (DL) models in the classification of active and non-a...
Quantifying interpretation reproducibility in Vision Transformer models with TAVAC.
Science advances
Dec 20, 2024
Deep learning algorithms can extract meaningful diagnostic features from biomedical images, promising improved patient care in digital pathology. Vision Transformer (ViT) models capture long-range spatial relationships and offer robust prediction pow...
Evaluation of a deep learning prostate cancer detection system on biparametric MRI against radiological reading.
European radiology
Dec 19, 2024
OBJECTIVES: This study aims to evaluate a deep learning pipeline for detecting clinically significant prostate cancer (csPCa), defined as Gleason Grade Group (GGG) ≥ 2, using biparametric MRI (bpMRI) and compare its performance with radiological read...
Reliability of post-contrast deep learning-based highly accelerated cardiac cine MRI for the assessment of ventricular function.
Magnetic resonance imaging
Dec 19, 2024
OBJECTIVE: The total examination time can be reduced if high-quality two-dimensional (2D) cine images can be collected post-contrast to minimize non-scanning time prior to late gadolinium-enhanced imaging. This study aimed to assess the equivalency o...
Image quality assessment and white matter hyperintensity quantification in two accelerated high-resolution 3D FLAIR techniques: Wave-CAIPI and deep learning-based SPACE.
Clinical radiology
Dec 19, 2024
AIM: To compare the image quality obtained using two accelerated high-resolution 3D fluid-attenuated inversion recovery (FLAIR) techniques for the brain-deep learning-reconstruction SPACE (DL-SPACE) and Wave-CAIPI FLAIR.
Deep learning-based intratumoral and peritumoral features for differentiating ocular adnexal lymphoma and idiopathic orbital inflammation.
European radiology
Dec 19, 2024
OBJECTIVES: To evaluate the value of deep-learning-based intratumoral and peritumoral features for differentiating ocular adnexal lymphoma (OAL) and idiopathic orbital inflammation (IOI).
Exploring transparency: A comparative analysis of explainable artificial intelligence techniques in retinography images to support the diagnosis of glaucoma.
Computers in biology and medicine
Dec 19, 2024
Machine learning models are widely applied across diverse fields, including nearly all segments of human activity. In healthcare, artificial intelligence techniques have revolutionized disease diagnosis, particularly in image classification. Although...
Feasibility/clinical utility of half-Fourier single-shot turbo spin echo imaging combined with deep learning reconstruction in gynecologic magnetic resonance imaging.
Abdominal radiology (New York)
Dec 18, 2024
BACKGROUND: When antispasmodics are unavailable, the periodically rotated overlapping parallel lines with enhanced reconstruction (PROPELLER; called BLADE by Siemens Healthineers) or half Fourier single-shot turbo spin echo (HASTE) is clinically used...
MHAGuideNet: a 3D pre-trained guidance model for Alzheimer's Disease diagnosis using 2D multi-planar sMRI images.
BMC medical imaging
Dec 18, 2024
BACKGROUND: Alzheimer's Disease is a neurodegenerative condition leading to irreversible and progressive brain damage, with possible features such as structural atrophy. Effective precision diagnosis is crucial for slowing disease progression and red...
Ensemble deep learning and EfficientNet for accurate diagnosis of diabetic retinopathy.
Scientific reports
Dec 18, 2024
Diabetic Retinopathy (DR) stands as a significant global cause of vision impairment, underscoring the critical importance of early detection in mitigating its impact. Addressing this challenge head-on, this study introduces an innovative deep learnin...
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