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Related Concept Videos

Imaging Studies IV: Magnetic Resonance Imaging01:27

Imaging Studies IV: Magnetic Resonance Imaging

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Introduction:Magnetic Resonance Imaging, or MRI, can include a specialized imaging technique of the urinary system known as Magnetic Resonance Urography (MRU). This radiation-free technique uses strong magnetic fields and radio waves to produce detailed images with the help of a computer. MRU is particularly effective for visualizing fluid-filled structures like the kidneys, ureters, and bladder.Applications of MRI in the Genitourinary SystemKidneys and Ureters: MRI detects tumors, cysts,...
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Magnetic Resonance Imaging01:24

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Magnetic resonance imaging (MRI) is a noninvasive medical imaging technique based on a phenomenon of nuclear physics discovered in the 1930s, in which matter exposed to magnetic fields and radio waves was found to emit radio signals. In 1970, a physician and researcher named Raymond Damadian noticed that malignant (cancerous) tissue gave off different signals than normal body tissue. He applied for a patent for the first MRI scanning device in clinical use by the early 1980s. The early MRI...
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Imaging Studies III: Computed Tomography01:27

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DefinitionComputed Tomography (CT) of the genitourinary (GU) tract is a non-invasive imaging modality that utilizes X-rays and computer processing to generate detailed cross-sectional images of the urinary system, encompassing the kidneys, ureters, bladder, and adjacent structures such as the adrenal glands.PurposeCT scans of the GU tract serve several diagnostic and therapeutic purposes, including:Diagnosis of Urinary Tract Diseases: Detects kidney stones, tumors, cysts, and congenital...
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Updated: Aug 30, 2025

Modeling Brain Metastases Through Intracranial Injection and Magnetic Resonance Imaging
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Multiconvolutional Transfer Learning for 3D Brain Tumor Magnetic Resonance Images.

S K B Sangeetha1, V Muthukumaran2, K Deeba3

  • 1Department of Computer Science and Engineering, SRM Institute of Science and Technology, Chennai, India.

Computational Intelligence and Neuroscience
|September 2, 2022
PubMed
Summary
This summary is machine-generated.

Multiconvolutional transfer learning (MCTL) enhances deep learning for small medical imaging datasets. This method improves brain tumor detection accuracy using 3D MRI scans, aiding clinical diagnosis.

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Machine Learning

Background:

  • Acquiring labeled medical imaging data is challenging and costly, hindering advanced analysis.
  • High-resolution, 3D, and multi-scale anatomical details in medical images increase analytical complexity.
  • Deep learning offers potential for automated workflows but requires substantial data.

Purpose of the Study:

  • To address the limitations of deep learning in small medical imaging datasets.
  • To introduce and evaluate Multiconvolutional Transfer Learning (MCTL) for medical image analysis.
  • To improve the accuracy of brain tumor classification using 3D MRI without contrast enhancement.

Main Methods:

  • Employed Multiconvolutional Transfer Learning (MCTL), a transfer learning approach for small datasets.
  • Utilized a convolutional autoencoder for classifying 3D Magnetic Resonance Imaging (MRI) brain tumor data.
  • Applied transfer learning by using an initial baseline to learn new features on a smaller target dataset.

Main Results:

  • MCTL demonstrated an accuracy increase of 1.5% in detecting small targets.
  • The MCTL approach facilitates more accurate classification of brain tumors in 3D MRI.
  • The study showed improved detection of small targets, crucial for early diagnosis.

Conclusions:

  • MCTL effectively enables deep learning on small medical imaging datasets.
  • This technique can enhance the accuracy of clinical diagnosis, particularly for brain tumor severity using MRI.
  • The research has broad applicability across various medical imaging and diagnostic procedures.