@inproceedings{fc850d5e018b4d48804188b2eb866ce1,
title = "GT-net: A deep learning network for gastric tumor diagnosis",
abstract = "Gastric cancer is one of the most common cancers, which causes the second largest number of deaths in the world. Traditional diagnosis approach requires pathologists to manually annotate the gastric tumor in gastric slice for cancer identification, which is laborious and time-consuming. In this paper, we proposed a deep learning based framework, namely GT-Net, for automatic segmentation of gastric tumor. The proposed GT-Net adopts different architectures for shallow and deep layers for better feature extraction. We evaluate the proposed framework on publicly available BOT gastric slice dataset. The experimental results show that our GT-Net performs better than state-of-the-art networks like FCN-8s, U-net, and achieved a new state-of-the-art F1 score of 90.88% for gastric tumor segmentation.",
keywords = "Fully Convolutional Network, Gastric Tumor, Segmentation",
author = "Yuexiang Li and Xinpeng Xie and Shaoxiong Liu and Xuechen Li and Linlin Shen",
note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 30th International Conference on Tools with Artificial Intelligence, ICTAI 2018 ; Conference date: 05-11-2018 Through 07-11-2018",
year = "2018",
month = dec,
day = "13",
doi = "10.1109/ICTAI.2018.00014",
language = "English",
series = "Proceedings - International Conference on Tools with Artificial Intelligence, ICTAI",
publisher = "IEEE Computer Society",
pages = "20--24",
booktitle = "Proceedings - 2018 IEEE 30th International Conference on Tools with Artificial Intelligence, ICTAI 2018",
address = "United States",
}
