  Radiology report generation (RRG) has gained increasing research attention
because of its huge potential to mitigate medical resource shortages and aid
the process of disease decision making by radiologists. Recent advancements in
RRG are largely driven by improving a model's capabilities in encoding
single-modal feature representations, while few studies explicitly explore the
cross-modal alignment between image regions and words. Radiologists typically
focus first on abnormal image regions before composing the corresponding text
descriptions, thus cross-modal alignment is of great importance to learn a RRG
model which is aware of abnormalities in the image. Motivated by this, we
propose a Class Activation Map guided Attention Network (CAMANet) which
explicitly promotes crossmodal alignment by employing aggregated class
activation maps to supervise cross-modal attention learning, and simultaneously
enrich the discriminative information. CAMANet contains three complementary
modules: a Visual Discriminative Map Generation module to generate the
importance/contribution of each visual token; Visual Discriminative Map
Assisted Encoder to learn the discriminative representation and enrich the
discriminative information; and a Visual Textual Attention Consistency module
to ensure the attention consistency between the visual and textual tokens, to
achieve the cross-modal alignment. Experimental results demonstrate that
CAMANet outperforms previous SOTA methods on two commonly used RRG benchmarks.
