Scale defect detection is an essential part of the quality control in the production process of medical syringes. Due to difficulty of collecting sufficient abnormal samples and defect types, it is impractical to optimize the deep learning model in a supervised learning manner for the defect detection of medical syringes. In this paper, we proposed an unsupervised defect detection method for medical syringes based on denoising convolutional autoencoder (DCAE). DCAE works as a deep reconstruction model, with a larger number of defect-free samples, to repair defects on anomaly samples reliably. The defects can be detected and located in the inspection phase by calculating the residual between the original and reconstructed images. The experimental results indicate that the proposed method is robust and can detect several scale defects in medical syringes. Our method reaches 95.11% average accuracy on one real-world medical syringe dataset, showing its practicality for defect detection.