Evaluating Fairness of Mask R-CNN for Kidney Infection Detection based on Renal Scintigraphy

doi:10.1109/BigData62323.2024.10825439

	Evaluating Fairness of Mask R-CNN for Kidney Infection Detection based on Renal Scintigraphy
	Jiayi Wang1 ; Mingyan Wu 2; Yuhang Guo1 ; Ha Wu 3; Zhiyu Wan1
	2024-12-18
会议录名称	2024 IEEE INTERNATIONAL CONFERENCE ON BIG DATA (BIGDATA)
ISSN	2639-1589
发表状态	已发表
DOI	10.1109/BigData62323.2024.10825439
摘要	99mTc-DMSA renal scan plays a crucial role in assessing functional abnormalities in the kidneys. A deep learning model, Mask R-CNN, showed much promise in diagnosing acute pyelonephritis, a type of kidney infection. This study evaluated the diagnostic performance and fairness of Mask R-CNN and Faster R-CNN using a 99mTc-DMSA renal dataset. The classification results showed that Mask R-CNN achieved an accuracy of 0.89, while Faster R-CNN reached an accuracy of 0.88. Both models demonstrated strong classification capabilities for kidney conditions. Furthermore, the analysis of fairness across sex and age groups indicated that neither model exhibited significant bias, thereby supporting their suitability for clinical applications. Future research should consider integrating more patient data to further enhance the diagnostic capabilities and fairness assessments of the models.
会议地点	Washington, DC, USA
会议日期	15-18 Dec. 2024
URL	查看原文
来源库	IEEE
文献类型	会议论文
条目标识符	https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/474152
专题	生物医学工程学院生物医学工程学院_硕士生生物医学工程学院_硕士生生物医学工程学院_PI研究组_万之瑜组
作者单位	1.School of Biomedical Engineering, ShanghaiTech University, Shanghai, China 2.Central Research Institute UIH Group, Shanghai, China 3.Department of Nuclear Medicine, Children’s Hospital of Fudan University, Shanghai, China
第一作者单位	生物医学工程学院
第一作者的第一单位	生物医学工程学院
推荐引用方式 GB/T 7714	Jiayi Wang,Mingyan Wu,Yuhang Guo,et al. Evaluating Fairness of Mask R-CNN for Kidney Infection Detection based on Renal Scintigraphy[C],2024.