AIMC Topic: Image Interpretation, Computer-Assisted

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Uncertainty-aware segmentation quality prediction via deep learning Bayesian Modeling: Comprehensive evaluation and interpretation on skin cancer and liver segmentation.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society Apr 13, 2025
Image segmentation is a critical step in computational biomedical image analysis, typically evaluated using metrics like the Dice coefficient during training and validation. However, in clinical settings without manual annotations, assessing segmenta...
Image Interpretation, Computer-Assisted Uncertainty Liver Neoplasms Image Processing, Computer-Assisted Bayes Theorem Skin Neoplasms Reproducibility of Results Deep Learning Humans
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Hybrid of DSR-GAN and CNN for Alzheimer disease detection based on MRI images.

Scientific reports Apr 13, 2025
In this paper, we propose a deep super-resolution generative adversarial network (DSR-GAN) combined with a convolutional neural network (CNN) model designed to classify four stages of Alzheimer's disease (AD): Mild Dementia (MD), Moderate Dementia (M...
Magnetic Resonance Imaging Deep Learning Humans Alzheimer Disease ROC Curve Neural Networks, Computer Image Interpretation, Computer-Assisted Image Processing, Computer-Assisted Algorithms
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Joint semi-supervised and contrastive learning enables domain generalization and multi-domain segmentation.

Medical image analysis Apr 12, 2025
Despite their effectiveness, current deep learning models face challenges with images coming from different domains with varying appearance and content. We introduce SegCLR, a versatile framework designed to segment images across different domains, e...
Tomography, Optical Coherence Supervised Machine Learning Image Interpretation, Computer-Assisted Humans Algorithms Deep Learning Imaging, Three-Dimensional
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HALSR-Net: Improving CNN Segmentation of Cardiac Left Ventricle MRI with Hybrid Attention and Latent Space Reconstruction.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society Apr 12, 2025
Accurate cardiac MRI segmentation is vital for detailed cardiac analysis, yet the manual process is labor-intensive and prone to variability. Despite advancements in MRI technology, there remains a significant need for automated methods that can reli...
Humans Neural Networks, Computer Reproducibility of Results Magnetic Resonance Imaging, Cine Image Processing, Computer-Assisted Heart Ventricles Image Interpretation, Computer-Assisted
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External Validation of an Artificial Intelligence Algorithm Using Biparametric MRI and Its Simulated Integration with Conventional PI-RADS for Prostate Cancer Detection.

Academic radiology Apr 11, 2025
PURPOSE: Prostate imaging reporting and data systems (PI-RADS) experiences considerable variability in inter-reader performance. Artificial Intelligence (AI) algorithms were suggested to provide comparable performance to PI-RADS for assessing prostat...
Humans Artificial Intelligence Algorithms Magnetic Resonance Imaging Sensitivity and Specificity Prostatic Neoplasms Male Image Interpretation, Computer-Assisted Retrospective Studies Middle Aged Radiology Information Systems Reproducibility of Results Aged
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IT: An interpretable transformer model for Alzheimer's disease prediction based on PET/MR images.

NeuroImage Apr 11, 2025
Alzheimer's disease (AD) represents a significant challenge due to its progressive neurodegenerative impact, particularly within an aging global demographic. This underscores the critical need for developing sophisticated diagnostic tools for its ear...
Magnetic Resonance Imaging Humans Multimodal Imaging Positron-Emission Tomography Brain Alzheimer Disease Image Interpretation, Computer-Assisted Female Aged Neuroimaging Male Deep Learning
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Prevalence of low-flow state by automated machine-learning 3D echocardiography in patients with moderate-to-severe aortic stenosis and normal ejection-fraction.

The international journal of cardiovascular imaging Apr 10, 2025
BACKGROUND: In aortic valve stenosis (AS), measurement of stroke volume index (SVi) by the 2D Doppler- technique is required to calculate the aortic valve area (AVA) and determine flow status but is prone to systematic errors.
Female Reproducibility of Results Aged Aged, 80 and over Male Ventricular Function, Left Humans Machine Learning Predictive Value of Tests Image Interpretation, Computer-Assisted Hemodynamics Echocardiography, Three-Dimensional Automation Echocardiography, Doppler Aortic Valve Stenosis Blood Flow Velocity Aortic Valve Stroke Volume Prevalence Severity of Illness Index
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MDAL: Modality-difference-based active learning for multimodal medical image analysis via contrastive learning and pointwise mutual information.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society Apr 9, 2025
Multimodal medical images reveal different characteristics of the same anatomy or lesion, offering significant clinical value. Deep learning has achieved widespread success in medical image analysis with large-scale labeled datasets. However, annotat...
Glioma Multimodal Imaging Ovarian Neoplasms Brain Neoplasms Deep Learning Image Processing, Computer-Assisted Image Interpretation, Computer-Assisted Meningioma Humans Female
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DIPathMamba: A domain-incremental weakly supervised state space model for pathology image segmentation.

Medical image analysis Apr 9, 2025
Accurate segmentation of pathology images plays a crucial role in digital pathology workflow. However, two significant issues exist with the present pathology image segmentation methods: (i) Most fully supervised models rely on dense pixel-level anno...
Algorithms Image Processing, Computer-Assisted Supervised Machine Learning Image Interpretation, Computer-Assisted Humans
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Unsupervised brain MRI tumour segmentation via two-stage image synthesis.

Medical image analysis Apr 5, 2025
Deep learning shows promise in automated brain tumour segmentation, but it depends on costly expert annotations. Recent advances in unsupervised learning offer an alternative by using synthetic data for training. However, the discrepancy between real...
Image Interpretation, Computer-Assisted Deep Learning Magnetic Resonance Imaging Unsupervised Machine Learning Humans Algorithms Brain Neoplasms
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