The application discloses a kind of gradient optimization method and
system of multitask
radiology report generation, it is related to
medical information technology field, including, obtaining
medical training data set containing image,
label and report text, construct and include visual
encoder, clinical constraint auxiliary task
branch and text decoder Multi-task
report generation model;Firstly, the
failure mechanism of linear scalarization is studied from the perspective of gradient dynamics, and it is formalized as the "double dilemma" of drift term bias and
diffusion term attenuation using the SDE framework, thereby revealing the geometric
root cause of suboptimality in RRG multitask optimization, and proposing CAME-Grad, a gradient optimization
algorithm designed specifically for multitask RRG. As an optimizer independent of the
backbone network, it can be integrated into various model architectures in a plug-and-play manner. On the MIMIC-CXR and IU X-
Ray datasets, CAME-Grad was extensively evaluated across eight representative RRG methods, and the results showed that its average
clinical performance improved by 2.3% and 1.9%, respectively.