@inproceedings{c5b4c5d9b06d4d01827093e04008645f,
title = "Applying of Adaptive Threshold Non-maximum Suppression to Pneumonia Detection",
abstract = "Hyper-parameters in deep learning are sensitive to prediction results. Non-maximum suppression (NMS) is an indispensable method for the object detection pipelines. NMS uses a pre-defined threshold algorithm to suppress the bounding boxes while their overlaps are not significant. We found that the pre-defined threshold is a hyper-parameter determined by empirical knowledge. We propose an adaptive threshold NMS that uses different thresholds to suppress the bounding boxes whose overlaps are not significant. The proposed adaptive threshold NMS algorithm provides improvements on Faster R-CNN with the AP metric on pneumonia dataset. Furthermore, we intend to propose more methods to optimize the hyper-parameters.",
keywords = "Adaptive threshold, Hyper-parameters, NMS, Pneumonia detection",
author = "Hao Teng and Huijuan Lu and Minchao Ye and Ke Yan and Zhigang Gao and Qun Jin",
note = "Funding Information: This work is supported by the National Natural Science Foundation of China under Grants No. 61272315, No. 61572164, No. 61701468, No. 61877015 and the project of education planning in Zhejiang under Grants No. 2018SCG005. Key Laboratory of Electromagnetic Wave Information Technology and Metrology of Zhejiang Province. Student research project of China Jiliang university No. 2019X22030. Funding Information: Acknowledgements. This work is supported by the National Natural Science Foundation of China under Grants No. 61272315, No. 61572164, No. 61701468, No. 61877015 and the project of education planning in Zhejiang under Grants No. 2018SCG005. Key Laboratory of Electromagnetic Wave Information Technology and Metrology of Zhe-jiang Province. Student research project of China Jiliang university No. 2019X22030. Publisher Copyright: {\textcopyright} 2020, Springer Nature Singapore Pte Ltd.; 14th International Conference on Bio-inspired Computing: Theories and Applications, BIC-TA 2019 ; Conference date: 22-11-2019 Through 25-11-2019",
year = "2020",
doi = "10.1007/978-981-15-3415-7_43",
language = "English",
isbn = "9789811534140",
series = "Communications in Computer and Information Science",
publisher = "Springer",
pages = "518--528",
editor = "Linqiang Pan and Jing Liang and Boyang Qu",
booktitle = "Bio-inspired Computing",
}