In this paper, we propose a malignant tumor candidate detection method with FDG-PET/CT images. We design our network based on the residual architecture. The network can learn from the metabolic information provided by PET images and the anatomical information provided by CT images simultaneously. Our method achieved a sensitivity of 0.993 at a false positive rate of 20. The performance is acceptable for the preliminary candidate detection.