Over the past decade, convolutional neural networks (CNN) have shown very competitive performance in medical image analysis tasks, such as disease classification, tumor segmentation, and lesion detection. CNN has great advantages in extracting local features of images. However, due to the locality of convolution operation, it cannot deal with long-range relationships well. Recently, transformers have been applied to computer vision and achieved remarkable success in large-scale datasets. Compared with natural images, multi-modal medical images have explicit and important long-range dependencies, and effective multi-modal fusion strategies can greatly improve the performance of deep models. This prompts us to study transformer-based structures and apply them to multi-modal medical images. Existing transformer-based network architectures require large-scale datasets to achieve better performance. However, medical imaging datasets are relatively small, which makes it difficult to apply pure transformers to medical image analysis. Therefore, we propose TransMed for multi-modal medical image classification. TransMed combines the advantages of CNN and transformer to efficiently extract low-level features of images and establish long-range dependencies between modalities. We evaluated our model on two datasets, parotid gland tumors classification and knee injury classification. Combining our contributions, we achieve an improvement of 10.1% and 1.9% in average accuracy, respectively, outperforming other state-of-the-art CNN-based models. The results of the proposed method are promising and have tremendous potential to be applied to a large number of medical image analysis tasks. To our best knowledge, this is the first work to apply transformers to multi-modal medical image classification.

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Due to the rapid rise of telemedicine, a lot of patients’ information will be transmitted through the Internet. However, the patients’ information is related to personal privacy, therefore, patients’ information needs to be encrypted when transmited and stored. Medical image encryption is a part of it. Due to the informative fine features of medical images, a common image encryption algorithm is no longer applied. Common encryption algorithm has a single theory based on chaos image encryption algorithm, other encryption algorithms are based on information entropy. However, the images processed with these cipher text encryption algorithm are cyclical, the outline is clear and the anti-tamper capability is not strong. In view of the bit being the smallest measure unit of pixel, in order to overcome the weakness from above algorithm, and take the advantage of the chaotic system, this paper will present a chaotic medical image encryption algorithm based on bit-plane decomposition. The paper combines the image encryption and chaotic system to improve the security. This way, it can increase the security of key space and image effectively. The histogram, pixel correlation, number of pixels change rate (NPCR) and other experimental results show that the algorithm satisfies the desired effect.

Abstract. Recently, advances in computers and high-speed communication tools have led to enhancements in remote medical consultation research. Laws in some localities require hospitals to encrypt patient information (including images of the patient) before transferring the data over a network. Therefore, developing suitable encryption algorithms is quite important for modern medicine. This paper demonstrates a digital image encryption algorithm based on chaotic mapping, which uses the no-period and no-convergence properties of a chaotic sequence to create image chaos and pixel averaging. Then, the chaotic sequence is used to encrypt the image, thereby improving data security. With this method, the security of data and images can be improved.