Melissa P. Upton, MD
Department of Pathology, University of Washington, Seattle

Faculty/Authors
Ewen D. McAlpine, FCPath(SA)
Division of Anatomical Pathology, School of Pathology, University of the Witwatersrand Johannesburg, SA  

CME/CMLE Credit: 1.0
Estimated Completion Time: 1 hour
Format: Online Educational Activity and Post Exam

Physician Competencies: Medical knowledge, patient care, practice-based learning

Eligibility for CME/CMLE credit: Max three attempts. You will have a maximum of three attempts to meet the following criteria:

• Module ≥80% = CME credit
• Module <80% = No CME credit, after a maximum of three attempts

Default Credit Type: None (You must meet the eligibility requirements in order to obtain CME credit.)

Accreditation Statement: The American Society for Clinical Pathology (ASCP) is accredited by the Accreditation Council for Continuing Medical Education to provide continuing medical education (CME) for physicians. This activity has been planned and implemented in accordance with the Essential Areas and Policies of the Accreditation Council for Continuing Medical Education (ACCME).

Credit Designation Statement: The American Society for Clinical Pathology (ASCP) designates this journal-based CME activity for a maximum of 1 AMA PRA Category 1 Credit(s)™. Physicians should claim only the credit commensurate with the extent of their participation in the activity.

Instructions

To claim CME/CMLE credit for the exercise, do the following:

1. Review the Technical Considerations.
2. Click Go to Course to view an overview of the modules in this course.
3. Click Access to begin the course.
4. Review the Educational Activity.
5. Complete and submit the Post Exam. You will have a maximum of three attempts to obtain the required score. You will be notified if you have met the credit requirements after each attempt.
6. Submit the course Evaluation.
7. Enter the maximum number of credits offered and click Claim CME/CMLE to register credit.

Faculty Disclosures​
The Journal CME editor and faculty have no relevant financial relationships with commercial interests to disclose.

Technical Considerations

Release Date: 12/10/2021
Review Date:
Expiration Date: 12/10/2024

• To compare the broad differences between supervised and unsupervised machine learning algorithms and their suitability for applications in Anatomic Pathology. 

• To appraise the use of clustering as a semi-supervised learning modality 

• To examine the general concept of generative adversarial networks and how these networks can improve supervised learning algorithms in Anatomic Pathology