Internet of Medical Things (IoMT) can connect many medical imaging equipments to the medical information network to facilitate the process of diagnosing and treating for doctors. As medical image contains sensitive information, it is of importance yet very challenging to safeguard the privacy or security of the patient. In this work, a deep learning based encryption and decryption network (DeepEDN) is proposed to fulfill the process of encrypting and decrypting the medical image. Specifically, in DeepEDN, the Cycle-Generative Adversarial Network (Cycle-GAN) is employed as the main learning network to transfer the medical image from its original domain into the target domain. Target domain is regarded as a “Hidden Factors" to guide the learning model for realizing the encryption. The encrypted image is restored to the original (plaintext) image through a reconstruction network to achieve an image decryption. In order to facilitate the data mining directly from the privacy-protected environment, a region of interest(ROI)-mining-network is proposed to extract the interested object from the encrypted image. The proposed DeepEDN is evaluated on the chest X-ray dataset. Extensive experimental results and security analysis show that the proposed method can achieve a high level of security with a good performance in efficiency.

The need for medical image encryption is increasingly pronounced, for example, to safeguard the privacy of the patients’ medical imaging data. In this article, a novel deep learning-based key generation network (DeepKeyGen) is proposed as a stream cipher generator to generate the private key, which can then be used for encrypting and decrypting of medical images. In DeepKeyGen, the generative adversarial network (GAN) is adopted as the learning network to generate the private key. Furthermore, the transformation domain (that represents the “style” of the private key to be generated) is designed to guide the learning network to realize the private key generation process. The goal of DeepKeyGen is to learn the mapping relationship of how to transfer the initial image to the private key. We evaluate DeepKeyGen using three data sets, namely, the Montgomery County chest X-ray data set, the Ultrasonic Brachial Plexus data set, and the BraTS18 data set. The evaluation findings and security analysis show that the proposed key generation network can achieve a high-level security in generating the private key.

It is well known that the Unet has been widely used in the area of medical image segmentation because of the cascade connection in the up-sampling process. But it does not perform well in dealing with complex medical images (Such as brain MRI). In order to achieve better segmentation performance by adopting the Unet, many researchers have paid more attention on stacking the Unet. However, the stacking process lead to a large increase in the amount of parameters. This is not a good choice when considering the tradeoff between precision and efficiency. Another problem is that as the depth of the network increases, the excessive loss of information is also a tricky problem. To address those problems, in this paper, we are trying to improve the network structure of Unet to make it more suitable for brain tumor segmentation. We propose a novel framework called Stack Multi-Connection Simple_ReducingNet(SMCSRNet) which are stacked by our basic blocks called Simple_ReducingNet(SRNet). The basic block SRNet is improved from the original Unet, which consists of four downsampling and upsampling operations during the encoding and decoding. Only one convolution operation is performed before each downsampling process. The operation of copy and crop are preserved between encoding and decoding. The main advantage of the SRNet is that the amount of parameters is reduced by 4/5 by comparing with the original Unet. Except for the problem of parameters number, we also proposed a series of bridge connections among the stacked cascade network to improve the loss of information. More specifically, some bridge connections will be adopted before the pooling operation in each layer during the downsampling process. It means that each layer in one basic block has a bridge connection with the same feature size from the previous basic block before pooling. And, it is worth noting that the training time of the proposed framework is much less than the original stacked Unet. Moreover, the performance of the proposed method is also improved compared to the stacked Unet. When further comparing with other state-of-the-art segmentation networks, it can be found that the performance is as good as the most popular DenseNet or ResNet. Overall, by evaluating the proposed framework on the BRAT2015, it can be proven that the proposed segmentation network has the ability to accurately extract the brain tumor boundary so as to obtain higher recognition quality with high efficiency.

Medical practitioners generally rely on multimodal brain images, for example based on the information from the axial, coronal, and sagittal views, to inform brain tumor diagnosis. Hence, to further utilize the 3D information embedded in such datasets, this paper proposes a multi-view dynamic fusion framework (hereafter, referred to as MVFusFra) to improve the performance of brain tumor segmentation. The proposed framework consists of three key building blocks. First, a multi-view deep neural network architecture, which represents multi learning networks for segmenting the brain tumor from different views and each deep neural network corresponds to multi-modal brain images from one single view. Second, the dynamic decision fusion method, which is mainly used to fuse segmentation results from multi-views into an integrated method. Then, two different fusion methods (i.e., voting and weighted averaging) are used to evaluate the fusing process. Third, the multi-view fusion loss (comprising segmentation loss, transition loss, and decision loss) is proposed to facilitate the training process of multi-view learning networks, so as to ensure consistency in appearance and space, for both fusing segmentation results and the training of the learning network. We evaluate the performance of MVFusFra on the BRATS 2015 and BRATS 2018 datasets. Findings from the evaluations suggest that fusion results from multi-views achieve better performance than segmentation results from the single view, and also implying effectiveness of the proposed multi-view fusion loss. A comparative summary also shows that MVFusFra achieves better segmentation performance, in terms of efficiency, in comparison to other competing approaches.