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This paper proposes a novel and efficient method to build a Computer-AidedDiagnoses (CAD) system for lung nodule detection based on Computed Tomography(CT). This task was treated as an Object Detection on Video (VID) problem byimitating how a radiologist reads CT scans. A lung nodule detector was trainedto automatically learn nodule features from still images to detect lung nodulecandidates with both high recall and accuracy. Unlike previous work which used3-dimensional information around the nodule to reduce false positives, wepropose two simple but efficient methods, Multi-slice propagation (MSP) andMotionless-guide suppression (MLGS), which analyze sequence information of CTscans to reduce false negatives and suppress false positives. We evaluated ourmethod in open-source LUNA16 dataset which contains 888 CT scans, and obtainedstate-of-the-art result (Free-Response Receiver Operating Characteristic scoreof 0.892) with detection speed (end to end within 20 seconds per patient on asingle NVidia GTX 1080) much higher than existing methods.

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