The utility of unsupervised machine learning in Anatomic Pathology

Online journal article in Anatomic Pathology that offers 1.0 continuing education (CME) credit that can be used to fulfill CC Part II requirements.

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Intended Audience: Pathologists, Residents, Clinical Scientists, Pathologist Assistants

Upon completion of this activity, you will be able to:

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

Intended Audience: Pathologists, Residents, Clinical Scientists, Pathologist Assistants

Upon completion of this activity, you will be able to:

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

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