Medical image description can be applied to clinical medical diagnosis, but the field still faces serious challenges. There is a serious problem of visual and textual data bias in medical datasets, which are the imbalanced distribution of health and disease data. This can greatly affect the learning performance of data-driven neural networks and finally lead to errors in the generated medical image descriptions. To address this problem, we propose a new medical image description network architecture named multimodal data-assisted knowledge fusion network (MDAKF), which introduces multimodal auxiliary signals to guide the Transformer network to generate more accurate medical reports. In detail, audio auxiliary signals provide clear abnormal visual regions to alleviate the visual data bias problem. However, the audio modality signals with similar pronunciation lack recognizability, which may lead to incorrect mapping of audio labels to medical image regions. Therefore, we further fuse the audio with text features as the auxiliary signal to improve the overall performance of the model. Through the experiments on two medical image description datasets, IU-X-ray and COV-CTR, it is found that the proposed model is superior to the previous models in terms of language generation evaluation indicators.

Frequent data breaches in the cloud environment have seriously affected cloud subscribers and providers. Privacy‐preserving image retrieval methods can improve the security of cloud image retrieval; however, existing methods have limited accuracy on dynamically updated image databases and mobile lightweight devices. In this study, we propose a privacy‐preserving image retrieval method based on disordered local histograms and vision transformer in cloud computing, by designing a multiple encryption method and transformer‐based feature model to better mine the local feature value of encrypted images. Specifically, the user performs different value substitution, position substitution, and color substitution on the subblocks of the image to protect the image information. The cloud server extracts the unordered local histogram from the encrypted image and generates retrievable features using transformer. Experiments show that compared with similar CNN schemes, the retrieval accuracy of this method is improved by 8.5%, and the retrieval efficiency is improved by 54.8%.