AIMC Topic: Image Interpretation, Computer-Assisted
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Cost-Sensitive Weighted Contrastive Learning Based on Graph Convolutional Networks for Imbalanced Alzheimer's Disease Staging.
IEEE transactions on medical imaging
Sep 3, 2024
Identifying the progression stages of Alzheimer's disease (AD) can be considered as an imbalanced multi-class classification problem in machine learning. It is challenging due to the class imbalance issue and the heterogeneity of the disease. Recentl...
Structure Embedded Nucleus Classification for Histopathology Images.
IEEE transactions on medical imaging
Sep 3, 2024
Nuclei classification provides valuable information for histopathology image analysis. However, the large variations in the appearance of different nuclei types cause difficulties in identifying nuclei. Most neural network based methods are affected ...
A retrospective evaluation of the potential of ChatGPT in the accurate diagnosis of acute stroke.
Diagnostic and interventional radiology (Ankara, Turkey)
Sep 2, 2024
PURPOSE: Stroke is a neurological emergency requiring rapid, accurate diagnosis to prevent severe consequences. Early diagnosis is crucial for reducing morbidity and mortality. Artificial intelligence (AI) diagnosis support tools, such as Chat Genera...
Automated MRI-based segmentation of intracranial arterial calcification by restricting feature complexity.
Magnetic resonance in medicine
Sep 2, 2024
PURPOSE: To develop an automated deep learning model for MRI-based segmentation and detection of intracranial arterial calcification.
ConvNext Mitosis Identification-You Only Look Once (CNMI-YOLO): Domain Adaptive and Robust Mitosis Identification in Digital Pathology.
Laboratory investigation; a journal of technical methods and pathology
Sep 2, 2024
In digital pathology, accurate mitosis detection in histopathological images is critical for cancer diagnosis and prognosis. However, this remains challenging due to the inherent variability in cell morphology and the domain shift problem. This study...
Assessment of multi-modal magnetic resonance imaging for glioma based on a deep learning reconstruction approach with the denoising method.
Acta radiologica (Stockholm, Sweden : 1987)
Sep 2, 2024
BACKGROUND: Deep learning reconstruction (DLR) with denoising has been reported as potentially improving the image quality of magnetic resonance imaging (MRI). Multi-modal MRI is a critical non-invasive method for tumor detection, surgery planning, a...
Assessing GPT-4 multimodal performance in radiological image analysis.
European radiology
Aug 30, 2024
OBJECTIVES: This study aims to assess the performance of a multimodal artificial intelligence (AI) model capable of analyzing both images and textual data (GPT-4V), in interpreting radiological images. It focuses on a range of modalities, anatomical ...
Can deep learning classify cerebral ultrasound images for the detection of brain injury in very preterm infants?
European radiology
Aug 30, 2024
OBJECTIVES: Cerebral ultrasound (CUS) is the main imaging screening tool in preterm infants. The aim of this work is to develop deep learning (DL) models that classify normal vs abnormal CUS to serve as a computer-aided detection tool providing timel...
Development and validation of a prediction model for malignant sinonasal tumors based on MR radiomics and machine learning.
European radiology
Aug 30, 2024
OBJECTIVES: This study aimed to utilize MR radiomics-based machine learning classifiers on a large-sample, multicenter dataset to develop an optimal model for predicting malignant sinonasal tumors and tumor-like lesions.
Deep learning for efficient reconstruction of highly accelerated 3D FLAIR MRI in neurological deficits.
Magma (New York, N.Y.)
Aug 30, 2024
OBJECTIVE: To compare compressed sensing (CS) and the Cascades of Independently Recurrent Inference Machines (CIRIM) with respect to image quality and reconstruction times when 12-fold accelerated scans of patients with neurological deficits are reco...
White Matter
Middle Aged
Deep Learning
Adolescent
Child, Preschool
Image Interpretation, Computer-Assisted
Aged
Humans
Child
Artifacts
Young Adult
Algorithms
Imaging, Three-Dimensional
Signal-To-Noise Ratio
Adult
Brain
Image Processing, Computer-Assisted
Nervous System Diseases
Male
Female
Magnetic Resonance Imaging
Reproducibility of Results
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