Enhancement of medical report generation by multi-scale image deblurring strategy

Automatic high-quality medical report generation plays an important role in the clinical diagnosis, which can assist and reduce the burden of doctors. Previous works often adopt the Transformer based structure and focus on the work of report generation process. Few studies consider the importance of image clarity by utilizing image deblurring to improve the quality of generated reports. We propose the multi-scale medical image deblurring network based on U-Net, improving the deblurring performance and recovering clear images gradually. Specially, the encoded representations of medical images and corresponding texts are aligned accurately by cross-modal feature alignment module. The experiments conducted on the IU-Xray and MIMIC-CXR datasets provide evidence that the proposed method outperforms previous studies in terms of both NLG metrics and Clinical Evaluation (CE) metrics, and the generated reports contain the professional medical terminology for higher quality.