AIMC Topic: Image Interpretation, Computer-Assisted
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Diagnostic Value of Magnetic Resonance Imaging Radiomics and Machine-learning in Grading Soft Tissue Sarcoma: A Mini-review on the Current State.
Academic radiology
Sep 10, 2024
Soft tissue sarcomas (STS) are a heterogeneous group of rare malignant tumors. Tumor grade might be underestimated in biopsy due to intratumoral heterogeneity. This mini-review aims to present the current state of predicting malignancy grades of STS ...
MYC Rearrangement Prediction From LYSA Whole Slide Images in Large B-Cell Lymphoma: A Multicentric Validation of Self-supervised Deep Learning Models.
Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc
Sep 10, 2024
Large B-cell lymphoma (LBCL) is a heterogeneous lymphoid malignancy in which MYC gene rearrangement (MYC-R) is associated with a poor prognosis, prompting the recommendation for more intensive treatment. MYC-R detection relies on fluorescence in situ...
Predicting Late Gadolinium Enhancement of Acute Myocardial Infarction in Contrast-Free Cardiac Cine MRI Using Deep Generative Learning.
Circulation. Cardiovascular imaging
Sep 10, 2024
BACKGROUND: Late gadolinium enhancement (LGE) cardiac magnetic resonance (CMR) is a standard technique for diagnosing myocardial infarction (MI), which, however, poses risks due to gadolinium contrast usage. Techniques enabling MI assessment based on...
Artificial intelligence and advanced MRI techniques: A comprehensive analysis of diffuse gliomas.
Journal of medical imaging and radiation sciences
Sep 9, 2024
INTRODUCTION: The complexity of diffuse gliomas relies on advanced imaging techniques like MRI to understand their heterogeneity. Utilizing the UCSF-PDGM dataset, this study harnesses MRI techniques, radiomics, and AI to analyze diffuse gliomas for o...
Artificial intelligence in Ultrasound: Pearls and pitfalls in 2024.
Ultraschall in der Medizin (Stuttgart, Germany : 1980)
Sep 6, 2024
Accelerated chemical shift encoded cardiovascular magnetic resonance imaging with use of a resolution enhancement network.
Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance
Sep 5, 2024
BACKGROUND: Cardiovascular magnetic resonance (CMR) chemical shift encoding (CSE) enables myocardial fat imaging. We sought to develop a deep learning network (fast chemical shift encoding [FastCSE]) to accelerate CSE.
Image Interpretation, Computer-Assisted
Adiposity
Magnetic Resonance Imaging, Cine
Predictive Value of Tests
Myocardium
Young Adult
Heart Diseases
Female
Adult
Deep Learning
Middle Aged
Humans
Reproducibility of Results
Male
Adipose Tissue
Case-Control Studies
Aged
Prospective Studies
Retrospective Studies
Comparison review of image classification techniques for early diagnosis of diabetic retinopathy.
Biomedical physics & engineering express
Sep 5, 2024
Diabetic retinopathy (DR) is one of the leading causes of vision loss in adults and is one of the detrimental side effects of the mass prevalence of Diabetes Mellitus (DM). It is crucial to have an efficient screening method for early diagnosis of DR...
BioFusionNet: Deep Learning-Based Survival Risk Stratification in ER+ Breast Cancer Through Multifeature and Multimodal Data Fusion.
IEEE journal of biomedical and health informatics
Sep 5, 2024
Breast cancer is a significant health concern affecting millions of women worldwide. Accurate survival risk stratification plays a crucial role in guiding personalised treatment decisions and improving patient outcomes. Here we present BioFusionNet, ...
H-Net: Heterogeneous Neural Network for Multi-Classification of Neuropsychiatric Disorders.
IEEE journal of biomedical and health informatics
Sep 5, 2024
Clinical studies have proved that both structural magnetic resonance imaging (sMRI) and functional magnetic resonance imaging (fMRI) are implicitly associated with neuropsychiatric disorders (NDs), and integrating multi-modal to the binary classifica...
A Vision Transformer-Based Framework for Knowledge Transfer From Multi-Modal to Mono-Modal Lymphoma Subtyping Models.
IEEE journal of biomedical and health informatics
Sep 5, 2024
Determining lymphoma subtypes is a crucial step for better patient treatment targeting to potentially increase their survival chances. In this context, the existing gold standard diagnosis method, which relies on gene expression technology, is highly...
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