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Abstract #2392

Prostate MRI Image Quality Control using Deep Learning

Jing Zhang1, Yang Song1, Ying Hou2, Yu-dong Zhang2, Xu Yan3, Yefeng Yao1, and Guang Yang1
1Shanghai Key Laboratory of Magnetic Resonance, Department of Physics, East China Normal University, shanghai, China, 2Department of Radiology, The First Affiliated Hospital with Nanjing Medical University,, Nanjing, China, 3MR Scientific Marketing, Siemens Healthcare, shanghai, China

Deep learning-based computer aided diagnosis (CAD) has been proposed to detect and classify prostate cancer lesions in multi-parametric Magnetic Resonance Imaging (mp-MRI) images. CAD requires their input images meet certain quality standards. In this work, we proposed a ResNet50-based model to filter out images not suitable as the input to the following lesion detection network. Taking unqualified images as positive cases, we obtained an area under ROC curve (AUC) of 0.8526 in test cohort, which helped to improve the performance of detection model and increased the interpretability by rejecting unqualified images with a reason instead of giving wrong results.

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