The invention provides a glomerular crescent
lesion multi-task semantic segmentation and classification
system, which relates to the technical field of medical
image processing and comprises an input module, an
encoder module, a decoder module, a boundary
branch module, a main output module and a
processing module. The input module is used for receiving images; the
encoder module adopts a symmetric topological structure and is used for carrying out multi-stage down-sampling operation on an input image and outputting feature maps of different scales; the decoder module is used for fusing the feature maps from the corresponding stages of the
encoder and carrying out up-sampling step by step; the boundary
branch module is connected behind the lowest layer output feature at the
tail end of the decoder; the main output module is located at the
tail end of the decoder; and the
processing module is used for calculating a composite
loss function and performing back propagation to update parameters of the encoder module, the decoder module and the boundary
branch module, and is used for outputting a final segmentation and
classification result of the
lesion region of the crescent body. The
system provided by the invention can realize
accurate segmentation and category prediction of the
lesion area of the crescent body.