<p>Designing an efficient and accurate anomaly detection method is crucial for quality control in medical products, particularly for identifying tiny and complex anomalies such as fur anomalies in medical syringes. In recent years, unsupervised anomaly detection methods based on reverse knowledge distillation have shown superior results. However, these methods suffer from the inability to prevent anomalous information from flowing through the student decoder during inference, leading to incorrect segmentation of abnormal areas. To address this issue, we propose a Multi-directional Feature Aggregation for unsupervised fur Anomaly Detection (MFAAD) method. Firstly, we design a Multi-directional Feature Aggregation (MFA) module, which consists of iterative feature shifting and aggregation operations. Each pixel in the feature map after being processed by the MFA module can acquire global pixel information, enhancing normal feature information while weakening abnormal ones, thereby blocking anomalous information from flowing through the student decoder. Additionally, we design a pseudo-anomaly mechanism based on the B-spline curve to generate line-like pseudo-anomalies, which guides the optimization of the MFA module. In the student decoder, we incorporate a Deep Hybrid Attention Module (DHAM) to enhance the feature extraction capabilities of the student decoder under complex scenarios. Our method achieves superior results of 100% image-level AUROC and 98.68%/95.4% pixel-level AUROC/PRO on one medical syringe dataset, outperforming other unsupervised anomaly detection methods. Experiments on two publicly available datasets, i.e., MVTec AD and Kolektor Surface-Defect Dataset (KSDD), further demonstrate the generality of our approach. Source code will be available at <a href="https://github.com/beibeifanfan/MFAAD">https://github.com/beibeifanfan/MFAAD</a></p>