Deep learning has allowed great progress to be made in obtaining more accurate prediction results for brain tumor segmentation. The current mainstream research approaches obtain segmentation accuracy improvements by modifying deep-learning model architectures, while ignoring the computational and storage efficiency issues of segmentation. In this paper, we proposed an improved knowledge distillation method: coordinate distillation (CD), which integrates channel and space information and completes brain tumor segmentation by training the student network with the teacher network without changing the original network architecture. Experimental results showed that the method was effective and that it could enhance the segmentation accuracy of brain tumors without changing the segmentation efficiency.

High-quality images are difficult to obtain in complex environments, such as underground or underwater. The low performance of images that are captured under low-light conditions significantly restricts the development of various engineering applications. However, existing algorithms exhibit color distortion or under/overexposure when addressing non-uniform illumination images. Furthermore, they introduce high-level noise when processing extremely dark images. In this paper, we propose a novel generative adversarial network (GAN) structure to generate high-quality enhanced images, which is called anti-attention block (AAB)-based generative adversarial networks (AABGAN). Specifically, we propose AAB to suppress undesired chromatic aberrations and establish a mapping relationship between different channels. The deep aggregation pyramid pooling module guides the network when combining multi-scale context information. Furthermore, we design a new multiple loss function to adjust images to the most suitable range for human vision. The results of extensive experiments show that our method outperforms state-of-the-art unsupervised image enhancement methods in terms of noise reduction and has a well-perceived result.