AIMC Topic: Image Interpretation, Computer-Assisted
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Robust brain MRI image classification with SIBOW-SVM.
Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Oct 24, 2024
Primary Central Nervous System tumors in the brain are among the most aggressive diseases affecting humans. Early detection and classification of brain tumor types, whether benign or malignant, glial or non-glial, is critical for cancer prevention an...
Accuracy of distinguishing benign, high-risk lesions and malignancies with inductive machine learning models in BIRADS 4 and BIRADS 5 lesions on breast MR examinations.
European journal of radiology
Oct 24, 2024
RATIONALE AND OBJECTIVES: The aim of this study is to explore the utility of Inductive Decision Tree models (IDTs) in distinguishing between benign, malignant, and high-risk (B3) breast lesions.
Clinical evaluation of accelerated diffusion-weighted imaging of rectal cancer using a denoising neural network.
European journal of radiology
Oct 24, 2024
BACKGROUND: To evaluate the effectiveness of a deep learning denoising approach to accelerate diffusion-weighted imaging (DWI) and thus improve diagnostic accuracy and image quality in restaging rectal MRI following total neoadjuvant therapy (TNT).
Deep learning super-resolution reconstruction for fast and high-quality cine cardiovascular magnetic resonance.
European radiology
Oct 23, 2024
OBJECTIVES: To compare standard-resolution balanced steady-state free precession (bSSFP) cine images with cine images acquired at low resolution but reconstructed with a deep learning (DL) super-resolution algorithm.
A Quantitative Comparison Between Human and Artificial Intelligence in the Detection of Focal Cortical Dysplasia.
Investigative radiology
Oct 23, 2024
OBJECTIVES: Artificial intelligence (AI) is thought to improve lesion detection. However, a lack of knowledge about human performance prevents a comparative evaluation of AI and an accurate assessment of its impact on clinical decision-making. The ob...
Evaluation of a Task-Specific Self-Supervised Learning Framework in Digital Pathology Relative to Transfer Learning Approaches and Existing Foundation Models.
Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc
Oct 23, 2024
An integral stage in typical digital pathology workflows involves deriving specific features from tiles extracted from a tessellated whole-slide image. Notably, various computer vision neural network architectures, particularly the ImageNet pretraine...
Knee Osteoarthritis SCAENet: Adaptive Knee Osteoarthritis Severity Assessment Using Spatial Separable Convolution with Attention-Based Ensemble Networks with Hybrid Optimization Strategy.
Journal of imaging informatics in medicine
Oct 22, 2024
Osteoarthritis (OA) of the knee is a chronic state that significantly lowers the quality of life for its patients. Early detection and lifetime monitoring of the progression of OA are necessary for preventive therapy. In the course of therapy, the Ke...
Addressing Challenges in Skin Cancer Diagnosis: A Convolutional Swin Transformer Approach.
Journal of imaging informatics in medicine
Oct 22, 2024
Skin cancer is one of the top three hazardous cancer types, and it is caused by the abnormal proliferation of tumor cells. Diagnosing skin cancer accurately and early is crucial for saving patients' lives. However, it is a challenging task due to var...
Searching Discriminative Regions for Convolutional Neural Networks in Fundus Image Classification With Genetic Algorithms.
IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Oct 22, 2024
Deep convolutional neural networks (CNNs) have been widely used for fundus image classification and have achieved very impressive performance. However, the explainability of CNNs is poor because of their black-box nature, which limits their applicati...
Accelerated cardiac cine with spatio-coil regularized deep learning reconstruction.
Magnetic resonance in medicine
Oct 21, 2024
PURPOSE: To develop an iterative deep learning (DL) reconstruction with spatio-coil regularization and multichannel k-space data consistency for accelerated cine imaging.
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