The application discloses a
coronary stenosis detection method based on
coronary angiography and space-time collaborative learning, and belongs to the field of intelligent
medical treatment. The deep
feature extraction network based on the channel mixing structure reparameterization
convolution single aggregation module is used, the channel segmentation and multi-
branch training are carried out, the feature reuse and information flow are enhanced, the double-flow path is introduced into the
feature fusion network to realize adaptive weighted fusion of different scale features, and the representation of spatial features is improved; the space-time collaborative analysis module capable of simultaneously fusing
time information and
local space information is used, the
time sequence connection is constructed, the space-time feature map is fused, the vascular shape deformation and hemodynamic information in the space-
time sequence are effectively captured, the effective feature information is enhanced, and therefore the detection accuracy and robustness of
coronary stenosis lesions are improved. The time and space information of the
coronary angiography sequence image is comprehensively considered, the number of false positive detections is reduced, and the detection capability of the model for
coronary stenosis lesions is comprehensively improved.