This paper reports an innovative approach to the classification of Stanford Type-A and Type-B aortic dissection using 3D CNN in conjunction with a novel Guided Attention mechanism which is capable of focusing on both global and local features and guiding the model to learn key representative of the lesion. The scheme has been modified such that inputs of grayscale images combining with EGA channels can be trained and fine-tuned like regular RGB image inputs, so the pre-trained model on RGB video sequences can be utilized. Finally, we demonstrate that our new approach significantly outperforms other attention methods.