Presented by Qiangqiang Gu, MS, PhD Candidate, University of
Minnesota
Breast cancer is one of the most common cancers in women. With early diagnosis, some breast cancers are highly curable. However, the concordance rate
of breast cancer diagnosis from histology slides by pathologists is unacceptably low. Classifying normal versus tumor breast tissues from breast histology microscopy images is an ideal case to use
for deep learning and could help to more reproducibly diagnose breast cancer. This session will discuss using 42 combinations of deep learning models, image data preprocessing techniques, and
hyperparameter configurations, with accuracy testing of tumor versus normal classification using the Breast Cancer Histology (BACH) dataset. Results of this process will be shared to demonstrate
preprocessing and hyperparameter configurations have a direct impact on the performance of deep neural networks for image classification.
This webinar is part of the 2022 Laboratory Webinar Series.
Laboratory Webinars
are a great, inexpensive way to provide continuing education to a large number of employees. The cost for each session is the same regardless of the number of attendees.
*Earn CEU's - One CEU per attendee per session
*Group Learning -
Unlimited # of participants for one low fee
*Archive Sessions - Archived materials are available for 1 year to
train new staff - AND STILL EARN CEUs!
Pricing for Individual Sessions
Early Bird (more than 30 days in advance) - $79.00
Regular (month of live event) - $99.00
Late (30 days after or
later) - $125.00
Looking to register for all 12 webinars at the best discount? Contact us!
Contact the NSH
Office, 443-535-4060 or histo@nsh.org.