Location
https://www.kennesaw.edu/ccse/events/computing-showcase/sp24-cday-program.php
Event Website
https://ksu-cday-spring2024.github.io
Document Type
Event
Start Date
25-4-2024 4:00 PM
Description
Our project aims to explore human tissue cells digitized by whole slide scanners for a better understanding of complex tumor microenvironments in breast cancer histopathology images, using various deep neural network models. First, we experimented with 70% percentages of tumor cells on image classification using ResNet50, VGG16, and Inception-ResNet. Second, we performed instance image segmentation using Mask-RCNN. Third, we applied two well-known explainable artificial intelligence (AI) techniques including Gradient-weighted Class Activation Mapping (Grad-CAM) and Shapley Additive Explanations (SHAP) to determine the effectiveness of the models.
Included in
UR-15 Analyzing Breast Cancer Histopathology Images Using Deep Neural Network Models
https://www.kennesaw.edu/ccse/events/computing-showcase/sp24-cday-program.php
Our project aims to explore human tissue cells digitized by whole slide scanners for a better understanding of complex tumor microenvironments in breast cancer histopathology images, using various deep neural network models. First, we experimented with 70% percentages of tumor cells on image classification using ResNet50, VGG16, and Inception-ResNet. Second, we performed instance image segmentation using Mask-RCNN. Third, we applied two well-known explainable artificial intelligence (AI) techniques including Gradient-weighted Class Activation Mapping (Grad-CAM) and Shapley Additive Explanations (SHAP) to determine the effectiveness of the models.
https://digitalcommons.kennesaw.edu/cday/Spring_2024/Undergraduate_Research/3