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Related Concept Videos

Computed Tomography01:10

Computed Tomography

Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
The technique was invented in the 1970s and is based on the principle that as X-rays pass through the body, they are absorbed or reflected at different levels. In the technique, a patient lies on a motorized platform while a computerized axial tomography (CAT) scanner rotates...
Magnetic Resonance Imaging01:24

Magnetic Resonance Imaging

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...
Imaging Studies III: Computed Tomography01:27

Imaging Studies III: Computed Tomography

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...
X-ray Imaging01:24

X-ray Imaging

German physicist Wilhelm Röntgen (1845–1923) was experimenting with electrical current when he discovered that a mysterious and invisible "ray" would pass through his flesh but leave an outline of his bones on a screen coated with a metal compound. In 1895, Röntgen made the first durable record of the internal parts of a living human: an "X-ray" image (as it came to be called) of his wife’s hand. Scientists worldwide quickly began their own experiments with X-rays, and by 1900, X-ray was widely...
Imaging Studies I: CT and MRI01:14

Imaging Studies I: CT and MRI

Introduction: MRI and CT scans are crucial advancements in medical imaging techniques, playing a vital role in diagnosing conditions related to the gastrointestinal (GI) system. Each scan serves distinct purposes, targets specific areas, and requires unique nursing duties.
Description of the Procedures
Computed Tomography (CT) scan:
Computed Tomography (CT) scans use X-ray technology to generate detailed images of bones, organs, and tissues. During the scan, the patient lies on a moving table...
Imaging Studies IV: Magnetic Resonance Imaging01:27

Imaging Studies IV: Magnetic Resonance Imaging

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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Domain Generalization Mitigates Scanner-Induced Domain Shift in Medical Imaging.

Dagoberto Pulido-Arias1, Mason C Cleveland1, Jay Patel1

  • 1Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, 149 13th Street, Charlestown, MA, 02129, USA.

Journal of Imaging Informatics in Medicine
|July 7, 2026
PubMed
Summary
This summary is machine-generated.

Domain generalization (DG) techniques improve AI performance on medical images across different scanners. While these methods enhance out-of-domain generalization, they don't fully match in-domain accuracy for tasks like prostate cancer grading and breast density assessment.

Keywords:
Deep learningDomain generalizationDomain shiftMammographyMedical imagingProstate MRI

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Area of Science:

  • Artificial Intelligence
  • Medical Imaging
  • Computer Vision

Background:

  • Deep learning models in medical imaging face performance degradation due to domain shift from varied acquisition hardware and protocols.
  • This limits the clinical deployment of AI tools for tasks like cancer grading and density assessment.

Purpose of the Study:

  • To comprehensively evaluate domain generalization (DG) techniques for mitigating performance degradation in medical image analysis.
  • To assess the effectiveness of DG methods on distinct clinical tasks using multi-institutional datasets.

Main Methods:

  • Evaluation of six DG algorithms against a baseline using a leave-one-domain-out protocol.
  • Testing on two tasks: grading prostate cancer aggressiveness from MRI (ProstateNet dataset) and assessing breast density from mammograms (DMIST dataset).

Main Results:

  • DG methods, especially those with explicit regularization, improved out-of-domain generalization.
  • The FISH algorithm achieved the highest out-of-domain AUROC (0.678) on the ProstateNet dataset, a significant improvement over the baseline (0.613).
  • Similar trends were observed on the DMIST dataset, though DG did not fully close the gap with in-domain performance.

Conclusions:

  • Domain generalization strategies are necessary for developing clinically deployable AI models in medical imaging.
  • While DG methods enhance robustness to domain shift, further research is needed to fully bridge the gap with in-domain performance.