  This paper explores the task of radiology report generation, which aims at
generating free-text descriptions for a set of radiographs. One significant
challenge of this task is how to correctly maintain the consistency between the
images and the lengthy report. Previous research explored solving this issue
through planning-based methods, which generate reports only based on high-level
plans. However, these plans usually only contain the major observations from
the radiographs (e.g., lung opacity), lacking much necessary information, such
as the observation characteristics and preliminary clinical diagnoses. To
address this problem, the system should also take the image information into
account together with the textual plan and perform stronger reasoning during
the generation process. In this paper, we propose an observation-guided
radiology report generation framework (ORGAN). It first produces an observation
plan and then feeds both the plan and radiographs for report generation, where
an observation graph and a tree reasoning mechanism are adopted to precisely
enrich the plan information by capturing the multi-formats of each observation.
Experimental results demonstrate that our framework outperforms previous
state-of-the-art methods regarding text quality and clinical efficacy
