Vision Transformer-Based Deep Learning for Histologic Classification of Endometrial Cancer

arXiv:2312.08479v2 Announce Type: replace
Abstract: Endometrial cancer, the fourth most common cancer in females in the United States, with the lifetime risk for developing this disease is approximately 2.8% in women. Precise histologic evaluation and molecular classification of endometrial cancer is important for effective patient management and determining the best treatment modalities. This study introduces EndoNet, which uses convolutional neural networks for extracting histologic features and a vision transformer for aggregating these features and classifying slides based on their visual …

abstract arxiv cancer classification cs.cv deep learning disease evaluation management patient risk transformer type united united states vision women