Semisupervised Attentive Mutualinfo Generative Adversarial Network for Brain Tumor Segmentation Recent years have witnessed tremendous success in medical image analysis using deep learning methods. However, most of the work are based on fullysupervised learning, which requires sizeable and reliable labeled data. This is one of the major bottlenecks in applying these methods into practice. To reduce the labeling burden, semisupervised learning that enables models to be learned from both labeled data and unlabeled data has been applied in many tasks, including medical image analysis. This work presents a semisupervised attentive mutual info generative adversarial network (SAMGAN) for brain tumor segmentation. SAMGAN is injected with comprehensive attention mechanism and a novel local mutual information maximization mechanism. The attention module contains channel attention block and spatial attention block, where the channel attention block indicates what to focus, while the spatial attention block indicates where to focus. Moreover, the mutual information maximization mechanism is used to focus the network on the local dependencies of the images, leading to more powerful representation ability of the network. Experimental results on two brain tumor segmentation tasks show that the proposed SAM GAN outperforms traditional fullysupervised methods and other semisupervised competitors, which shows the effectiveness of the proposed model.

SemiSupervised AttentionGuided CycleGAN for Data Augmentation on Medical Images Recently, deep learning methods, in particular, convolutional neural networks (CNNs), have made a massive breakthrough in computer vision. And a big amount of annotated data is the essential cornerstone to reach this success. However, in the medical domain, it is usually difficult (and sometimes even impossible) to get sufficient data for some specific learning tasks. Consequently, in this work, a novel data augmentation solution, called semisupervised attentionguided CycleGAN (SSACycleGAN) is proposed to resolve this problem. Specifically, a cycleconsistency GANsbased model is first proposed to generate synthetic tumor (resp., normal) images from normal (resp., tumor) images. Then, a semisupervised attention module is further proposed to enhance the modelu0027s capability in learning the important details of the training images, which in turns help the generated synthetic images become more realistic. To verify its effectiveness, experimental studies are conducted on three medical image datasets with limited amounts of MRI images, and the proposed SSACycleGAN is applied to generate synthetic tumor and normal MRI images for data augmentation. Experimental results show that i) SSACycleGAN can add (resp., remove) tumor lesions on (resp., from) the original normal (resp., tumor) images and generate very realistic synthetic tumor (resp. normal) images; and ii) in the ResNet18based MRI image classification tasks based on these datasets, data augmentation using SSACycleGAN achieves much better classification performances than the classic data augmentation methods.