AIMC Topic: Image Interpretation, Computer-Assisted
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Z-SSMNet: Zonal-aware Self-supervised Mesh Network for prostate cancer detection and diagnosis with Bi-parametric MRI.
Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Feb 15, 2025
Bi-parametric magnetic resonance imaging (bpMRI) has become a pivotal modality in the detection and diagnosis of clinically significant prostate cancer (csPCa). Developing AI-based systems to identify csPCa using bpMRI can transform prostate cancer (...
Diagnosis of Thyroid Nodule Malignancy Using Peritumoral Region and Artificial Intelligence: Results of Hand-Crafted, Deep Radiomics Features and Radiologists' Assessment in Multicenter Cohorts.
Journal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine
Feb 15, 2025
OBJECTIVE: To develop, test, and externally validate a hybrid artificial intelligence (AI) model based on hand-crafted and deep radiomics features extracted from B-mode ultrasound images in differentiating benign and malignant thyroid nodules compare...
A Bi-modal Temporal Segmentation Network for Automated Segmentation of Focal Liver Lesions in Dynamic Contrast-enhanced Ultrasound.
Ultrasound in medicine & biology
Feb 14, 2025
OBJECTIVE: To develop and validate an automated deep learning-based model for focal liver lesion (FLL) segmentation in a dynamic contrast-enhanced ultrasound (CEUS) video.
VSR-Net: Vessel-Like Structure Rehabilitation Network With Graph Clustering.
IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Feb 14, 2025
The morphologies of vessel-like structures, such as blood vessels and nerve fibres, play significant roles in disease diagnosis, e.g., Parkinson's disease. Although deep network-based refinement segmentation and topology-preserving segmentation metho...
A quantitatively interpretable model for Alzheimer's disease prediction using deep counterfactuals.
NeuroImage
Feb 13, 2025
Deep learning (DL) for predicting Alzheimer's disease (AD) has provided timely intervention in disease progression yet still demands attentive interpretability to explain how their DL models make definitive decisions. Counterfactual reasoning has rec...
Detection of brain tumors using a transfer learning-based optimized ResNet152 model in MR images.
Computers in biology and medicine
Feb 13, 2025
Brain tumors are incredibly harmful and can drastically reduce life expectancy. Most researchers use magnetic resonance (MR) scans to detect tumors because they can provide detailed images of the affected area. Recently, AI-based deep learning method...
Image quality and diagnostic performance of deep learning reconstruction for diffusion- weighted imaging in 3 T breast MRI.
European journal of radiology
Feb 12, 2025
PURPOSE: This study aimed to assess the image quality and the diagnostic value of deep learning reconstruction (DLR) for diffusion-weighted imaging (DWI) compared with conventional single-shot echo-planar imaging (ss-EPI) in 3 T breast MRI.
Automation bias in AI-assisted detection of cerebral aneurysms on time-of-flight MR angiography.
La Radiologia medica
Feb 12, 2025
PURPOSE: To determine how automation bias (inclination of humans to overly trust-automated decision-making systems) can affect radiologists when interpreting AI-detected cerebral aneurysm findings in time-of-flight magnetic resonance angiography (TOF...
Cross prior Bayesian attention with correlated inception and residual learning for brain tumor classification using MR images (CB-CIRL Net).
Journal of neuroscience methods
Feb 11, 2025
BACKGROUND: Brain tumor classification from magnetic resonance (MR) images is crucial for early diagnosis and effective treatment planning. However, the homogeneity of tumors across different categories poses a challenge. Although, attention-based co...
Artificial intelligence in digital pathology - time for a reality check.
Nature reviews. Clinical oncology
Feb 11, 2025
The past decade has seen the introduction of artificial intelligence (AI)-based approaches aimed at optimizing several workflows across many medical specialties. In clinical oncology, the most promising applications include those involving image anal...
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