The invention discloses a coronary
artery stenosis detection method based on
coronary angiography and space-time collaborative learning, and belongs to the field of intelligent
medical treatment. According to the method, through a deep
feature extraction network based on a channel
shuffling structure re-parameterization
convolution single aggregation module, through channel segmentation and multi-
branch training, feature
multiplexing and information flowing are enhanced, a double-flow path is introduced into a
feature fusion network to realize adaptive weighted fusion of features of different scales, and the
feature fusion efficiency is improved. The representation of spatial features is improved; a space-time collaborative analysis module capable of fusing
time information and
local space information at the same time is adopted, and
blood vessel shape deformation and hemodynamic information in a space-
time sequence are effectively captured and effective feature information is enhanced by constructing
time sequence connection and fusing a space-time feature graph, so that the detection accuracy and robustness of coronary
artery stenosis lesions are improved. According to the method, the time and space information of the
coronary angiography sequence image is comprehensively considered, the number of false positive detection is reduced, and the detection capability of the model coronary
artery stenosis lesion is comprehensively improved.