AIMC Topic: Image Interpretation, Computer-Assisted

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A Fusion Model of MRI Deep Transfer Learning and Radiomics for Discriminating between Pilocytic Astrocytoma and Adamantinomatous Craniopharyngioma.

Academic radiology
RATIONALE AND OBJECTIVES: This study aimed to develop and validate a fusion model combining MRI deep transfer learning (DTL) and radiomics for discriminating between pilocytic astrocytoma (PA) and adamantinomatous craniopharyngioma (ACP) in the sella...

Noncontrast MRI-based machine learning and radiomics signature can predict the severity of primary lower limb lymphedema.

Journal of vascular surgery. Venous and lymphatic disorders
OBJECTIVE: According to International Lymphology Society guidelines, the severity of lymphedema is determined by the difference in volume between the affected limb and the healthy side divided by the volume of the healthy side. However, this method o...

L-MAE: Longitudinal masked auto-encoder with time and severity-aware encoding for diabetic retinopathy progression prediction.

Computers in biology and medicine
Pre-training strategies based on self-supervised learning (SSL) have demonstrated success as pretext tasks for downstream tasks in computer vision. However, while SSL methods are often domain-agnostic, their direct application to medical imaging is c...

Conditional generative diffusion deep learning for accelerated diffusion tensor and kurtosis imaging.

Magnetic resonance imaging
PURPOSE: The purpose of this study was to develop DiffDL, a generative diffusion probabilistic model designed to produce high-quality diffusion tensor imaging (DTI) and diffusion kurtosis imaging (DKI) metrics from a reduced set of diffusion-weighted...

A Context-Dependent CNN-Based Framework for Multiple Sclerosis Segmentation in MRI.

International journal of neural systems
Despite several automated strategies for identification/segmentation of Multiple Sclerosis (MS) lesions in Magnetic Resonance Imaging (MRI) being developed, they consistently fall short when compared to the performance of human experts. This emphasiz...

DCA-Enhanced Alzheimer's detection with shearlet and deep learning integration.

Computers in biology and medicine
Alzheimer's dementia (AD) is a neurodegenerative disorder that affects the central nervous system, causing the cells to stop working or die. The quality of life for individuals with AD steadily declines over time. While current treatments can relieve...

Deep learning for segmentation of colorectal carcinomas on endoscopic ultrasound.

Techniques in coloproctology
BACKGROUND: Bowel-preserving local resection of early rectal cancer is less successful if the tumor infiltrates the muscularis propria as opposed to submucosal infiltration only. Magnetic resonance imaging currently lacks the spatial resolution to pr...

Personalized predictions of Glioblastoma infiltration: Mathematical models, Physics-Informed Neural Networks and multimodal scans.

Medical image analysis
Predicting the infiltration of Glioblastoma (GBM) from medical MRI scans is crucial for understanding tumor growth dynamics and designing personalized radiotherapy treatment plans. Mathematical models of GBM growth can complement the data in the pred...

Glaucoma detection: Binocular approach and clinical data in machine learning.

Artificial intelligence in medicine
In this work, we present a multi-modal machine learning method to automate early glaucoma diagnosis. The proposed methodology introduces two novel aspects for automated diagnosis not previously explored in the literature: simultaneous use of ocular f...