Breast cancer (BCa) is a type of disease that has multiple prognostic markers that differs from one cancer stage to another. Assessing the area and pattern of cancer regions is essential for pathological investigations. However, the main purpose of this study is to segment the regions of invasive carcinoma (i.e., non-tubular and tubular) in the histological sections of BCa. The segmentation was performed on hematoxylin and eosin (H&E)-stained tissue slides of 42 BCa patients from 5 different centers in Korea. The tumor-stroma ratio (TSR) is a promising prognostic parameter for BCa as well as in other epithelial cancer types estimating the area of stroma and tumor regions. Therefore, in this paper, we used the pre-trained convolutional neural network (CNN) models as a backbone of UNet, to precisely extract the tumor regions from the stroma tissue components for TSR analysis.
|Title of host publication||International Conference on Electrical, Computer, and Energy Technologies, ICECET 2022|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Publication status||Published - 2022|
|Event||2022 IEEE International Conference on Electrical, Computer, and Energy Technologies, ICECET 2022 - Prague, Czech Republic|
Duration: 2022 Jul 20 → 2022 Jul 22
|Name||International Conference on Electrical, Computer, and Energy Technologies, ICECET 2022|
|Conference||2022 IEEE International Conference on Electrical, Computer, and Energy Technologies, ICECET 2022|
|Period||22/7/20 → 22/7/22|
Bibliographical noteFunding Information:
ACKNOWLEDGMENT This research was financially supported by the Ministry of Trade, Industry, and Energy (MOTIE), Korea, under the “Regional Specialized Industry Development Program (R&D, P0002072)” supervised by the Korea Institute for Advancement of Technology (KIAT).
© 2022 IEEE.
All Science Journal Classification (ASJC) codes
- Artificial Intelligence
- Computer Science Applications
- Computer Vision and Pattern Recognition
- Energy Engineering and Power Technology
- Electrical and Electronic Engineering
- Media Technology