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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...
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...

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Related Experiment Video

Updated: May 31, 2026

Automation of the Micronucleus Assay Using Imaging Flow Cytometry and Artificial Intelligence
09:11

Automation of the Micronucleus Assay Using Imaging Flow Cytometry and Artificial Intelligence

Published on: January 27, 2023

AutoCumulus: an automated mammographic density measure created using artificial intelligence.

Osamah Al-Qershi1, Tuong L Nguyen2, Michael S Elliott3,4

  • 1Centre for Epidemiology & Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, VIC, Australia. o.alqershi@unimelb.edu.au.

BMC Cancer
|May 28, 2026
PubMed
Summary
This summary is machine-generated.

A new deep learning tool, AutoCumulus, accurately measures mammographic density, a breast cancer risk factor. It shows high repeatability and better prediction of interval cancers than existing methods.

Keywords:
Breast cancerBreast densityDeep learningMachine learning

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Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
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13:44

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Published on: August 30, 2013

Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Oncology

Background:

  • Mammographic density is a key breast cancer risk factor.
  • Previous measurements used varied quantitative and automated methods.
  • AutoCumulus is a novel automated measure developed using deep learning.

Purpose of the Study:

  • To introduce and evaluate AutoCumulus, a new deep learning-based automated mammographic density measurement.
  • To compare AutoCumulus performance against established methods like CUMULUS and LIBRA.
  • To assess AutoCumulus's accuracy, repeatability, and predictive value for interval cancers.

Main Methods:

  • A deep learning regression model (ConvNeXtSmall) was trained on mammograms from 9,057 women.
  • Performance was assessed by correlating estimated and measured percent density.
  • Independent testing used the CSAW-CC dataset to compare AutoCumulus with LIBRA for interval cancer prediction.

Main Results:

  • AutoCumulus achieved a high correlation (0.95) with human density measures.
  • It demonstrated superior performance to LIBRA in within-woman repeatability and interval cancer prediction (AUC 0.638 vs 0.597).
  • AutoCumulus showed higher specificity for 95% sensitivity compared to LIBRA.

Conclusions:

  • AutoCumulus is a highly accurate and rapid automated mammographic density measurement tool.
  • It offers improved repeatability and modest enhancement in predicting interval cancers over existing automated measures.
  • Further validation is needed, but AutoCumulus shows potential as a scalable breast cancer risk indicator.