Improving Analysis of Breast Microcalcifications

Current Status
Not Enrolled
Price
Free With Unlimited Credits
Get Started
  • Approver: AHRA®
  • ARRT®: Accepted
  • Expiration Date: 11/30/25
  • Credit Hours: 1 Category A Credits

Course Acceptance List

  • ARRT: Accepted
  • Florida: Cat A / Technical
  • Texas: Direct
  • NMTCB: Accepted
Image of breast cancer - AI X ray

CQR / Structured Education – AI X ray

All CQR / SE Credits Listed in PDF Format with Links to Courses

Course Description And Objectives:

Objectives

  • Understand the significance of MCs in early breast cancer detection.
  • Comprehend how CADe systems aid in pinpointing MCs in mammograms.
  • Master key machine learning methodologies for improved detection of MCs.
  • Keep up to date with the latest trends and advancements in the field of breast cancer detection technology.

Course Description

Understand the complexity of breast cancer detection in this comprehensive course that goes into the dynamic interaction of computer-aided detection (CADe) systems and machine learning. Focusing on the crucial role of microcalcifications (MCs) in breast cancer prognosis, we explore innovative techniques like deep convolutional neural networks, blob detection algorithms, and dense regression models. You’ll learn how these tools enhance mammographic accuracy, reduce false positives, and heighten malignancy classification capabilities. The course provides a unique blend of theory and hands-on application, employing real-world data sets to reinforce learning. Additionally, you will gain insights into emerging research trends and the future of technology in cancer detection.

This activity may be available in multiple formats or from different sponsors. A self-learning activity can be completed only once per biennium.  ARRT® CE Requirements

Improving Analysis of Breast Microcalcifications Course – AI X ray