We propose a student-teacher network with skip connections (Skip-ST) which is trained by the direct reverse knowledge distillation (DRKD) to realize anomaly detection. Skip-ST consists of a pretrained teacher encoder and a randomly initialized student decoder. The output of the teacher encoder’s last layer is the input of the student decoder, which aims to recover the multi-scale representation extracted by the teacher encoder. We introduce skip connections between the teacher encoder and student decoder to prevent the student from missing normal information. The experimental results show that Skip-ST achieves a 7.95% AUROC improvement averagely on five medical datasets.