Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Sinusoidal Sources01:18

Sinusoidal Sources

1.1K
Direct current (DC) refers to an electric current that flows in a single direction, maintaining a constant polarity. This is in contrast to alternating current (AC), which periodically changes its direction and magnitude. AC forms the backbone of modern electricity transmission and distribution systems due to its efficient long-distance transmission capabilities.
In homes, the power supplies use sinusoidal sources to provide electricity. These sources generate a voltage that varies sinusoidally...
1.1K
Source Transformation01:15

Source Transformation

11.2K
Source transformation is a fundamental technique employed in circuit analysis, offering a valuable tool for simplifying complex electrical circuits. This technique involves the replacement of either a voltage source in series with a resistor by a current source in parallel with a resistor, or vice versa. The key concept here is that when the original sources are deactivated (turned off), the equivalent resistance at the circuit's end terminals remains the same.
It is essential to note that when...
11.2K
AC Sources01:20

AC Sources

4.0K
Direct current is a flow of electric charge in only one direction and has a steady state of constant voltage in the circuit. Rectifiers, batteries, commutator-equipped generators, and fuel cells are some examples of devices that generate direct current. Nowadays, most applications use a time-varying voltage source. Alternating current is a flow of electric charge that periodically reverses direction. An alternating current is produced by an alternating emf that is generated in a power plant. If...
4.0K
Sources of Law01:26

Sources of Law

1.8K
Laws form the essential rules set by governing authorities to shape and control societal behavior. In nursing, laws guide actions, safeguard patient rights, define nurses' scope of practice, and maintain professional standards. Understanding the legal framework governing nursing involves recognizing four primary sources of law: constitutional, statutory, administrative (regulatory), and common law.
Constitutional law is foundational, deriving from federal and state constitutions, and...
1.8K
Independent and Dependent Sources01:18

Independent and Dependent Sources

2.5K
In electrical circuits, sources play a crucial role in providing power for the operation of the circuit. These sources can be broadly categorized into two types: independent and dependent.
Independent voltage or current sources supply a fixed amount of voltage or current, respectively, which is unaffected by other elements within the circuit. These are represented using specific symbols. Independent voltage sources are symbolized with polarities (+ and -), indicating the direction of the...
2.5K
RC Circuit without Source01:16

RC Circuit without Source

2.4K
When a DC source is abruptly disconnected from an RC (Resistor-Capacitor) circuit, the circuit becomes source-free. Assuming that the capacitor was fully charged before the source was removed, its initial voltage, denoted as V0, can be considered as the initial energy that stimulates the circuit.
Applying Kirchhoff's current law at the top node of the circuit and substituting the current values across the components, a first-order differential equation is obtained. By rearranging the terms...
2.4K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

BAC quantification complements rather than competes with PREVENT in real-world cardiovascular risk assessment.

European heart journal·2026
Same author

Postdeployment Monitoring and Surveillance Methods, Guidelines, and Possibilities for AI in Radiology.

Radiographics : a review publication of the Radiological Society of North America, Inc·2026
Same author

Artificial Intelligence for Digital Breast Tomosynthesis Screening with and without Prior Examinations in BreastScreen Norway.

Radiology. Artificial intelligence·2026
Same author

Leveraging artificial intelligence for equitable women's health outcomes through imaging.

The British journal of radiology·2026
Same author

The Emory Knee Radiograph (MRKR) Dataset.

Journal of imaging informatics in medicine·2026
Same author

Artificial intelligence-based quantification of breast arterial calcifications to predict cardiovascular morbidity and mortality.

European heart journal·2026
Same journal

Impact of Exposure Parameters on Deep Learning Models in Chest Radiography and Implications for Deployment.

Radiology. Artificial intelligence·2026
Same journal

Impact on Cost and Expert Time of Data-Efficient Deep Learning for Medical Image Segmentation.

Radiology. Artificial intelligence·2026
Same journal

Benchmarking of AI and Radiologists for Indeterminate Lung Nodule Malignancy Risk Estimation on Screening CT: The LUNA25 Challenge.

Radiology. Artificial intelligence·2026
Same journal

When One Sequence Is Enough-And When It Isn't.

Radiology. Artificial intelligence·2026
Same journal

Cracking the Registration Conundrum in Breast MRI: Preserving the Tumor Signal to Reveal True Treatment Change.

Radiology. Artificial intelligence·2026
Same journal

Toward Personalized Care of Intracranial Aneurysms.

Radiology. Artificial intelligence·2026
See all related articles

Related Experiment Video

Updated: Jan 22, 2026

Achieving Efficient Fragment Screening at XChem Facility at Diamond Light Source
08:35

Achieving Efficient Fragment Screening at XChem Facility at Diamond Light Source

Published on: May 29, 2021

7.0K

Open-Source Dataset for the RSNA Screening Mammography Cancer Detection Challenge.

Hari M Trivedi1, Maryam Vazirabad2, Felipe C Kitamura3

  • 1Department of Radiology and Imaging Sciences, Emory University School of Medicine, 1364 Clifton Rd NE, Atlanta, GA 30322.

Radiology. Artificial Intelligence
|January 21, 2026
PubMed
Summary
This summary is machine-generated.

This dataset provides mammograms and outcomes for cancer detection research. It aids in developing and validating AI tools for improved breast cancer screening.

Keywords:
BreastInformaticsMammography

More Related Videos

Mining Spatial Transcriptomics Datasets using DeepSpaceDB
10:16

Mining Spatial Transcriptomics Datasets using DeepSpaceDB

Published on: September 5, 2025

677
Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
03:37

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

1.3K

Related Experiment Videos

Last Updated: Jan 22, 2026

Achieving Efficient Fragment Screening at XChem Facility at Diamond Light Source
08:35

Achieving Efficient Fragment Screening at XChem Facility at Diamond Light Source

Published on: May 29, 2021

7.0K
Mining Spatial Transcriptomics Datasets using DeepSpaceDB
10:16

Mining Spatial Transcriptomics Datasets using DeepSpaceDB

Published on: September 5, 2025

677
Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
03:37

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

1.3K

Area of Science:

  • Radiology
  • Medical Imaging
  • Oncology

Background:

  • Mammography is a key tool for breast cancer screening.
  • Accurate cancer detection in mammograms is crucial for patient outcomes.
  • Large, annotated datasets are essential for developing advanced diagnostic tools.

Purpose of the Study:

  • To introduce the RSNA Screening Mammography Cancer Detection Challenge dataset.
  • To provide a comprehensive resource for research in mammographic cancer detection.
  • To facilitate the development and benchmarking of artificial intelligence algorithms for breast cancer screening.

Main Methods:

  • The dataset comprises mammograms from screening participants.
  • Includes associated metadata and confirmed pathological outcomes.
  • Data is curated for a challenge focused on cancer detection.

Main Results:

  • The dataset enables evaluation of algorithm performance on real-world mammographic data.
  • Facilitates comparison of different detection and diagnostic approaches.
  • Supports research into subtle cancer detection in screening mammography.

Conclusions:

  • The RSNA dataset is a valuable resource for advancing mammography-based cancer detection.
  • It supports the development of AI-driven tools to enhance screening accuracy.
  • Promotes collaborative research in medical imaging and artificial intelligence for oncology.