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arXiv · 1712.05114

Detection and Attention: Diagnosing Pulmonary Lung Cancer from CT by Imitating Physicians

Ning Li·Haopeng Liu·Bin Qiu·Wei Guo·Shijun Zhao·Kungang Li·Jie He
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Abstract

This paper proposes a novel and efficient method to build a Computer-Aided Diagnoses (CAD) system for lung nodule detection based on Computed Tomography (CT). This task was treated as an Object Detection on Video (VID) problem by imitating how a radiologist reads CT scans. A lung nodule detector was trained to automatically learn nodule features from still images to detect lung nodule candidates with both high recall and accuracy. Unlike previous work which used 3-dimensional information around the nodule to reduce false positives, we propose two simple but efficient methods, Multi-slice propagation (MSP) and Motionless-guide suppression (MLGS), which analyze sequence information of CT scans to reduce false negatives and suppress false positives. We evaluated our method in open-source LUNA16 dataset which contains 888 CT scans, and obtained state-of-the-art result (Free-Response Receiver Operating Characteristic score of 0.892) with detection speed (end to end within 20 seconds per patient on a single NVidia GTX 1080) much higher than existing methods.

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Ning Li, Haopeng Liu, Bin Qiu, Wei Guo, Shijun Zhao, Kungang Li, Jie He. 2017-12-14. Detection and Attention: Diagnosing Pulmonary Lung Cancer from CT by Imitating Physicians. https://arxiv.org/abs/1712.05114

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