Abstract by Jacob Stern

Personal Infomation

Presenter's Name

Jacob Stern


Allison Lambert
Junseong Ahn
Wilson Redd
York Westenhaver

Degree Level



Allison Lambert
Brad Hatch
Wilson Redd
York Westenhaver
Junseong Ahn

Abstract Infomation


Computer Science

Faculty Advisor

David Wingate


Siamese Network Pre-training and Attention-based CNN for Whole-slide Medical Image Classification


Currently, cervical cancer in India has a mortality rate of forty nine percent. With over one hundred and sixty million women at risk of cervical cancer, there is a great need for more and faster screening of women ages thirty to fifty-nine. One challenge to achieving this goal is a lack of qualified pathologists to review the Pap Smears. Obtaining, staining, shipping and returning the sample for diagnosis is a time consuming process. In an effort to decrease the time required to diagnose a patient, we are creating a convolutional neural network to detect cancerous cervical cells. The difficulty in this task is classification of data with low signal-to-noise ratio — in a 80,000 x 20,000 pixel medical slide image, the relevant cancerous area might be only a few pixels wide. Furthermore, we attempt to identify these areas using only whole slide labels. We use two techniques to train a neural network for this task. One, we are using a morphological siamese network for pre-training that is more sensitive to cancer.  Two, we use an attention mechanism to hone the attention of the CNN on the most important regions of the image.