@inproceedings{5cbe2f7bb59f4f63bab1091851c40697,
title = "Combining the Transformers and CNNs for Renal Parenchymal Tumors Diagnosis",
abstract = "Convolutional neural networks (CNNs) can be used to automatically classify the subtypes of renal parenchymal tumors. However, such approaches may overlook the long-range dependencies of features. In this study, we stacked Transformers and CNNs to efficiently capture both long-range dependencies of features and low-level spatial details. The optimal stack layout, the width, and the depth of the networks were determined according to the characteristics of the renal tumors. In addition, we adopted transfer learning for better performance. The experiments conducted on the T2-weighted magnetic resonance (T2W-MR) images from 199 patients presented an 82.9% overall classification accuracy and 0.96 average AUC. The results demonstrate that combining CNN with Transforms is an effective strategy for renal parenchymal tumors diagnosis.",
keywords = "Convolutional Neural Network (CNN), Magnetic Resonance Imaging (MRI), renal tumors, transformer",
author = "Shuai Wang and Zhang, {Lian Yu} and Du, {Hui Qian} and Yan Chen and Yao Zheng and Wenbo Mei",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 10th International Conference on Bioinformatics and Computational Biology, ICBCB 2022 ; Conference date: 13-05-2022 Through 15-05-2022",
year = "2022",
doi = "10.1109/ICBCB55259.2022.9802126",
language = "English",
series = "2022 10th International Conference on Bioinformatics and Computational Biology, ICBCB 2022",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "56--60",
booktitle = "2022 10th International Conference on Bioinformatics and Computational Biology, ICBCB 2022",
address = "United States",
}