The invention provides a
pathological image analysis method based on cross-scale spatial constraint fusion and local
perception. The method comprises the steps of 1, data preprocessing and
feature extraction; step 2, executing a two-way fusion strategy based on spatial constraint to obtain fused enhanced features; step 3, capturing high-order
semantic information between scales by using a bilinear fusion mode based on a
Hadamard product; 4, performing residual connection on the enhanced features and the fused features to serve as input of a 10-time
branch and a 20-time
branch; 5, remodeling the features after residual connection into a two-dimensional shape, and inputting three convolutional
layers which are arranged in a layered manner and expanded layer by layer; and 6, further performing gating adaptive fusion on the global semantic features obtained after the 10-time branches and the 20-time branches are subjected to the step 5. On the basis of easy deployment, feature representation with high expression ability is obtained, information between branches of different scales is fully utilized, and better performance is obtained in downstream tasks.