AIMC Topic: Magnetic Resonance Imaging

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Performance of deep learning in classifying malignant primary and metastatic brain tumors using different MRI sequences: A medical analysis study.

Journal of X-ray science and technology
BACKGROUND: Malignant Primary Brain Tumor (MPBT) and Metastatic Brain Tumor (MBT) are the most common types of brain tumors, which require different management approaches. Magnetic Resonance Imaging (MRI) is the most frequently used modality for asse...

Deep convolutional neural network based hyperspectral brain tissue classification.

Journal of X-ray science and technology
BACKGROUND: Hyperspectral brain tissue imaging has been recently utilized in medical research aiming to study brain science and obtain various biological phenomena of the different tissue types. However, processing high-dimensional data of hyperspect...

FNSAM: Image super-resolution using a feedback network with self-attention mechanism.

Technology and health care : official journal of the European Society for Engineering and Medicine
BACKGROUND: High-resolution (HR) magnetic resonance imaging (MRI) provides rich pathological information which is of great significance in diagnosis and treatment of brain lesions. However, obtaining HR brain MRI images comes at the cost of extending...

Deep Learning Based on Enhanced MRI T1 Imaging to Differentiate Small-cell and Non-small-cell Primary Lung Cancers in Patients with Brain Metastases.

Current medical imaging
OBJECTIVES: To differentiate the primary small-cell lung cancer (SCLC) and non-small-cell lung cancer (NSCLC) for patients with brain metastases (BMs) based on a deep learning (DL) model using contrast-enhanced magnetic resonance imaging (MRI) T1 wei...

Deep Learning for Predicting Distant Metastasis in Patients with Nasopharyngeal Carcinoma Based on Pre-Radiotherapy Magnetic Resonance Imaging.

Combinatorial chemistry & high throughput screening
IMPORTANCE: Accurate pre-treatment prediction of distant metastasis in patients with Nasopharyngeal Carcinoma (NPC) enables the implementation of appropriate treatment strategies for high-risk individuals.

[A generative adversarial network-based unsupervised domain adaptation method for magnetic resonance image segmentation].

Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi
Intelligent medical image segmentation methods have been rapidly developed and applied, while a significant challenge is domain shift. That is, the segmentation performance degrades due to distribution differences between the source domain and the ta...

The deep radiomic analytics pipeline.

Veterinary radiology & ultrasound : the official journal of the American College of Veterinary Radiology and the International Veterinary Radiology Association
Radiomics refers to the process of extracting useful imaging features from radiological data. Conventional radiomics like standard uptake value, intensity histograms, or phase images involve hand-crafted (manual) or automated regions of interest (com...

Prediction of Decompensation and Death in Advanced Chronic Liver Disease Using Deep Learning Analysis of Gadoxetic Acid-Enhanced MRI.

Korean journal of radiology
OBJECTIVE: This study aimed to evaluate the usefulness of quantitative indices obtained from deep learning analysis of gadoxetic acid-enhanced hepatobiliary phase (HBP) MRI and their longitudinal changes in predicting decompensation and death in pati...