  Radiology report generation aims to automatically generate detailed and
coherent descriptive reports alongside radiology images. Previous work mainly
focused on refining fine-grained image features or leveraging external
knowledge. However, the precise alignment of fine-grained image features with
corresponding text descriptions has not been considered. This paper presents a
novel method called Fine-grained Image-Text Aligner (FITA) to construct
fine-grained alignment for image and text features. It has three novel designs:
Image Feature Refiner (IFR), Text Feature Refiner (TFR) and Contrastive Aligner
(CA). IFR and TFR aim to learn fine-grained image and text features,
respectively. We achieve this by leveraging saliency maps to effectively fuse
symptoms with corresponding abnormal visual regions, and by utilizing a
meticulously constructed triplet set for training. Finally, CA module aligns
fine-grained image and text features using contrastive loss for precise
alignment. Results show that our method surpasses existing methods on the
widely used benchmark
