AIMC Topic: Magnetic Resonance Imaging
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Predicting progression-free survival in sarcoma using MRI-based automatic segmentation models and radiomics nomograms: a preliminary multicenter study.
Skeletal radiology
Dec 4, 2024
OBJECTIVES: Some sarcomas are highly malignant, associated with high recurrence despite treatment. This multicenter study aimed to develop and validate a radiomics signature to estimate sarcoma progression-free survival (PFS).
Development and Validation of Multiparametric MRI-based Interpretable Deep Learning Radiomics Fusion Model for Predicting Lymph Node Metastasis and Prognosis in Rectal Cancer: A Two-center Study.
Academic radiology
Dec 4, 2024
RATIONALE AND OBJECTIVES: To develop interpretable machine learning models that utilize deep learning (DL) and radiomics based on multiparametric Magnetic resonance imaging (MRI) to predict preoperative lymph node (LN) metastasis in rectal cancer.
UDA-GS: A cross-center multimodal unsupervised domain adaptation framework for Glioma segmentation.
Computers in biology and medicine
Dec 4, 2024
Gliomas are the most common and malignant form of primary brain tumors. Accurate segmentation and measurement from MRI are crucial for diagnosis and treatment. Due to the infiltrative growth pattern of gliomas, their labeling is very difficult. In tu...
Prediction of Brain Cancer Occurrence and Risk Assessment of Brain Hemorrhage Using Hybrid Deep Learning Technique.
Cancer investigation
Dec 4, 2024
The prediction of brain cancer occurrence and risk assessment of brain hemorrhage using a hybrid deep learning (DL) technique is a critical area of research in medical imaging analysis. One prominent challenge in this field is the accurate identifica...
Multi-Sensor Learning Enables Information Transfer Across Different Sensory Data and Augments Multi-Modality Imaging.
IEEE transactions on pattern analysis and machine intelligence
Dec 4, 2024
Multi-modality imaging is widely used in clinical practice and biomedical research to gain a comprehensive understanding of an imaging subject. Currently, multi-modality imaging is accomplished by post hoc fusion of independently reconstructed images...
Functional Connectivity Biomarker Extraction for Schizophrenia Based on Energy Landscape Machine Learning Techniques.
Sensors (Basel, Switzerland)
Dec 4, 2024
Brain connectivity represents the functional organization of the brain, which is an important indicator for evaluating neuropsychiatric disorders and treatment effects. Schizophrenia is associated with impaired functional connectivity but characteriz...
Revolutionizing healthcare: a comparative insight into deep learning's role in medical imaging.
Scientific reports
Dec 4, 2024
Recently, Deep Learning (DL) models have shown promising accuracy in analysis of medical images. Alzeheimer Disease (AD), a prevalent form of dementia, uses Magnetic Resonance Imaging (MRI) scans, which is then analysed via DL models. To address the ...
Deep learning analysis of fMRI data for predicting Alzheimer's Disease: A focus on convolutional neural networks and model interpretability.
PloS one
Dec 4, 2024
The early detection of Alzheimer's Disease (AD) is thought to be important for effective intervention and management. Here, we explore deep learning methods for the early detection of AD. We consider both genetic risk factors and functional magnetic ...
Tensor dictionary-based heterogeneous transfer learning to study emotion-related gender differences in brain.
Neural networks : the official journal of the International Neural Network Society
Dec 3, 2024
In practice, collecting auxiliary labeled data with same feature space from multiple domains is difficult. Thus, we focus on the heterogeneous transfer learning to address the problem of insufficient sample sizes in neuroimaging. Viewing subjects, ti...
A Machine learning classification framework using fused fractal property feature vectors for Alzheimer's disease diagnosis.
Brain research
Dec 3, 2024
Alzheimer's disease (AD) profoundly affects brain tissue and network structures. Analyzing the topological properties of these networks helps to understand the progression of the disease. Most studies focus on single-scale brain networks, but few add...
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