  Automatic radiology report generation is essential to computer-aided
diagnosis. Through the success of image captioning, medical report generation
has been achievable. However, the lack of annotated disease labels is still the
bottleneck of this area. In addition, the image-text data bias problem and
complex sentences make it more difficult to generate accurate reports. To
address these gaps, we pre-sent a self-guided framework (SGF), a suite of
unsupervised and supervised deep learning methods to mimic the process of human
learning and writing. In detail, our framework obtains the domain knowledge
from medical reports with-out extra disease labels and guides itself to extract
fined-grain visual features as-sociated with the text. Moreover, SGF
successfully improves the accuracy and length of medical report generation by
incorporating a similarity comparison mechanism that imitates the process of
human self-improvement through compar-ative practice. Extensive experiments
demonstrate the utility of our SGF in the majority of cases, showing its
superior performance over state-of-the-art meth-ods. Our results highlight the
capacity of the proposed framework to distinguish fined-grained visual details
between words and verify its advantage in generating medical reports.
