@inproceedings{0f8b332465e04f019aed7d83e75f9268,
title = "OpenMedIA: Open-Source Medical Image Analysis Toolbox and Benchmark Under Heterogeneous AI Computing Platforms",
abstract = "In this paper, we present OpenMedIA, an open-source toolbox library containing a rich set of deep learning methods for medical image analysis under heterogeneous Artificial Intelligence (AI) computing platforms. Various medical image analysis methods, including 2D/3D medical image classification, segmentation, localisation, and detection, have been included in the toolbox with PyTorch and/or MindSpore implementations under heterogeneous NVIDIA and Huawei Ascend computing systems. To our best knowledge, OpenMedIA is the first open-source algorithm library providing compared PyTorch and MindSpore implementations and results on several benchmark datasets. The source codes and models are available at https://git.openi.org.cn/OpenMedIA.",
author = "Zhuang, \{Jia Xin\} and Xiansong Huang and Yang Yang and Jiancong Chen and Yue Yu and Wei Gao and Ge Li and Jie Chen and Tong Zhang",
note = "Publisher Copyright: {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.; 5th Chinese Conference on Pattern Recognition and Computer Vision, PRCV 2022 ; Conference date: 04-11-2022 Through 07-11-2022",
year = "2022",
doi = "10.1007/978-3-031-18907-4\_28",
language = "English",
isbn = "9783031189067",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "356--367",
editor = "Shiqi Yu and Jianguo Zhang and Zhaoxiang Zhang and Tieniu Tan and Yuen, \{Pong C.\} and Yike Guo and Junwei Han and Jianhuang Lai",
booktitle = "Pattern Recognition and Computer Vision - 5th Chinese Conference, PRCV 2022, Proceedings",
address = "Germany",
}