Attention mechanisms that focus on salient parts have been widely used in image captioning to provide visual explanations for the rationale of deep learning networks. In addition, more recent works focus on generating long-form text instead of single sentences. Moreover, variations of RNN, such as long–short-term memory (LSTM) and gated recurrent unit (GRU), that contain different controlling gates capable of learning information from a long time ago, are frequently employed in effectively capturing the semantics of image captioning tasks. In practice, the values obtained via the activation function at one suitable layer of the objects recognition CNN are considered as the visual feature vector. However, generating diagnostic reports is a challenging task due to the complexity and diversity of objects in medical images. Hence, it is essential to explore the automatic diagnosis of images and the generation of reports to improve the interpretability of deep learning. Teaching machines to automatically write diagnostic reports is a semantic and effective way to support the interpretability of deep learning models. In addition, while deep learning, with its advantage of end-to-end processing, has emerged on a large scale in recent medical diagnosis studies, the non-interpretable network and non-standardized evaluation make deep learning like a black box. Automatic diagnostic report generation from medical images is an indispensable trend to reduce this workload. Therefore, analyzing and depicting textual reports, which require skilled experience, can be a time-consuming and stressful task for professionals. Although one report may seem simple, containing only indications, findings and impression, there are many patients with unforeseen abnormal medical images. An example of such a report can be seen in Fig. As we all know, a detailed explanation of medical images such as CT (computed tomography), ultrasound, MRI (magnetic resonance imaging), or pathological imaging must be conducted by professional physicians or pathologists who write a diagnostic report for each patient.
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