  Automatically generating radiology reports can improve current clinical
practice in diagnostic radiology. On one hand, it can relieve radiologists from
the heavy burden of report writing; On the other hand, it can remind
radiologists of abnormalities and avoid the misdiagnosis and missed diagnosis.
Yet, this task remains a challenging job for data-driven neural networks, due
to the serious visual and textual data biases. To this end, we propose a
Posterior-and-Prior Knowledge Exploring-and-Distilling approach (PPKED) to
imitate the working patterns of radiologists, who will first examine the
abnormal regions and assign the disease topic tags to the abnormal regions, and
then rely on the years of prior medical knowledge and prior working experience
accumulations to write reports. Thus, the PPKED includes three modules:
Posterior Knowledge Explorer (PoKE), Prior Knowledge Explorer (PrKE) and
Multi-domain Knowledge Distiller (MKD). In detail, PoKE explores the posterior
knowledge, which provides explicit abnormal visual regions to alleviate visual
data bias; PrKE explores the prior knowledge from the prior medical knowledge
graph (medical knowledge) and prior radiology reports (working experience) to
alleviate textual data bias. The explored knowledge is distilled by the MKD to
generate the final reports. Evaluated on MIMIC-CXR and IU-Xray datasets, our
method is able to outperform previous state-of-the-art models on these two
datasets.
