AIMC Topic: Magnetic Resonance Imaging
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An ensemble approach using multidimensional convolutional neural networks in wavelet domain for schizophrenia classification from sMRI data.
Scientific reports
Mar 25, 2025
Schizophrenia is a complicated mental condition marked by disruptions in thought processes, perceptions, and emotional responses, which can cause severe impairment in everyday functioning. sMRI is a non-invasive neuroimaging technology that visualize...
Machine learning based radiomics approach for outcome prediction of meningioma - a systematic review.
F1000Research
Mar 25, 2025
INTRODUCTION: Meningioma is the most common brain tumor in adults. Magnetic resonance imaging (MRI) is the preferred imaging modality for assessing tumor outcomes. Radiomics, an advanced imaging technique, assesses tumor heterogeneity and identifies ...
Optimizing imaging modalities for sarcoma subtypes in radiation therapy: State of the art.
Critical reviews in oncology/hematology
Mar 24, 2025
The choice of imaging modalities is essential in sarcoma management, as different techniques provide complementary information depending on tumor subtype and anatomical location. This narrative review examines the role of imaging in sarcoma character...
Establishment of a deep-learning-assisted recurrent nasopharyngeal carcinoma detecting simultaneous tactic (DARNDEST) with high cost-effectiveness based on magnetic resonance images: a multicenter study in an endemic area.
Cancer imaging : the official publication of the International Cancer Imaging Society
Mar 24, 2025
BACKGROUND: To investigate the feasibility of detecting local recurrent nasopharyngeal carcinoma (rNPC) using unenhanced magnetic resonance images (MRI) and optimize a layered management strategy for follow-up with a deep learning model.
Brain tumor intelligent diagnosis based on Auto-Encoder and U-Net feature extraction.
PloS one
Mar 24, 2025
Preoperative classification of brain tumors is critical to developing personalized treatment plans, however existing classification methods rely on manual intervention and often have problems with efficiency and accuracy, which may lead to misdiagnos...
TCDE-Net: An unsupervised dual-encoder network for 3D brain medical image registration.
Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Mar 23, 2025
Medical image registration is a critical task in aligning medical images from different time points, modalities, or individuals, essential for accurate diagnosis and treatment planning. Despite significant progress in deep learning-based registration...
SpineMamba: Enhancing 3D spinal segmentation in clinical imaging through residual visual Mamba layers and shape priors.
Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Mar 22, 2025
Accurate segmentation of three-dimensional (3D) clinical medical images is critical for the diagnosis and treatment of spinal diseases. However, the complexity of spinal anatomy and the inherent uncertainties of current imaging technologies pose sign...
Evaluating the prognostic significance of artificial intelligence-delineated gross tumor volume and prostate volume measurements for prostate radiotherapy.
Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
Mar 22, 2025
BACKGROUND AND PURPOSE: Artificial intelligence (AI) may extract prognostic information from MRI for localized prostate cancer. We evaluate whether AI-derived prostate and gross tumor volume (GTV) are associated with toxicity and oncologic outcomes a...
Machine learning-based radiomics using MRI to differentiate early-stage Duchenne and Becker muscular dystrophy in children.
BMC musculoskeletal disorders
Mar 22, 2025
OBJECTIVES: Duchenne muscular dystrophy (DMD) and Becker muscular dystrophy (BMD) present similar symptoms in the early stage, complicating their differentiation. This study aims to develop a classification model using radiomic features from MRI T2-w...
Deep learning on T2WI to predict the muscle-invasive bladder cancer: a multi-center clinical study.
Scientific reports
Mar 22, 2025
To develop a deep learning (DL) model based on MRI to predict muscle-invasive bladder cancer (MIBC). A total of 559 patients, including 521 patients in our center and 38 patients in external centers were collected from 2012 to 2023 to construct the D...
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