## 2 Related Work  
Medical image description generation has great practical significance, which attracts many researchers to carry out targeted exploration. At present, medical image description models based on deep learning methods are all based on an encoder-decoder structure (as shown in Figure 1), and deep CNN and RNN are utilized in the encoder-decoder structure of these models [15]. In fact, CNN extracts significant features from the input medical images in the form of fixed-length vectors. RNN or its variant networks, such as long short-term memory (LSTM) and gated reentry unit (GRU), are used as language models [16] to translate fixed-length vectors obtained from CNN into semantically and syntactically correct disease diagnosis texts. [PERSON] et al. [17] applied the coding-decoder structure for the first time to the task of generating medical diagnostic reports for chest X-rays, and the sentences generated by this method were mainly descriptions of disease classification and detection. At the coding end, they selected network-network [18] and Google network [19] CNN to extract image features and classify them, and obtained 17 disease labels corresponding to MESH statements. These terms were the most commonly used sentences to describe chest diseases without obtaining repeated results from other MESH terms. Although [PERSON] et al. [17] obtained a better performance on Google, only short sentences of five words were generated to describe classified diseases, rather than a fluent and coherent report. [PERSON] et al. [20] modified the basic framework of [PERSON] et al. [16] to generate a description of retinopathy. They used a pre-trained CNN as an encoder, using exit and integration techniques to encode 37,000 fundus images, resulting in feature coding vectors that were fed into the LSTM and decoded into a caption. The method of [PERSON] et al. [20] only generated the description of abnormal features in the images, and the description of disease information was not detailed enough. The subtitle generation model of ultrasound images by [PERSON] et al. [21] was divided into two modules, an encoder module and a language generation module, and the two modules needed to be trained separately. They used a region detection model, Faster-RCNN [22], as an encoder to extract disease features and were able to locate diseased areas in ultrasound medical images. Finally, the language generation module generated the description of the critical area of the lesion and could generate the prediction box to locate the lesion area. Compared with other models, this model had the advantages of a short training time and fewer parameters, but the disadvantage was that the prediction error rate of language and words was high in the generated description statements. The decoding phase of the above methods could not obtain enough useful information, and finally the decoding of the sentence could not reach its ideal effect. Therefore, many researchers have begun to add attention mechanisms to the coding-decoding structure. For example, [PERSON] et al. [23] proposed the MDNet model, which innovatively added attention mechanisms to the description task of medical images. The attention module they used was able to increase the model's ability to extract features, successfully enabling direct mapping from bladder cancer images to diagnostic reports. They used ResNet as an encoder to extract image features and extended it to skip connections to solve the problem of disappearing gradients during training. The image description model they proposed achieved a relatively advanced performance, but the generated diagnostic report only contained the description words of five bladder features, not a complete bladder radiology film diagnostic report. [PERSON] et al. [24] proposed an image description model with a co-attention mechanism that divided images into regions of the same size, extracted visual features from the last convolutional layer of VGG-19 [25], and calculated the weighted sum of semantic features and visual features, respectively.