  Deep learning is widely applied in computer-aided pathological diagnosis,
which alleviates the pathologist workload and provide timely clinical analysis.
However, most models generally require large-scale annotated data for training,
which faces challenges due to the sampling and annotation scarcity in
pathological images. The rapid developing generative models shows potential to
generate more training samples from recent studies. However, they also struggle
in generalization diversity with limited training data, incapable of generating
effective samples. Inspired by the pathological transitions between different
stages, we propose an adaptive depth-controlled diffusion (ADD) network to
generate pathological progressive images for effective data augmentation. This
novel approach roots in domain migration, where a hybrid attention strategy
guides the bidirectional diffusion, blending local and global attention
priorities. With feature measuring, the adaptive depth-controlled strategy
ensures the migration and maintains locational similarity in simulating the
pathological feature transition. Based on tiny training set (samples less than
500), the ADD yields cross-domain progressive images with corresponding
soft-labels. Experiments on two datasets suggest significant improvements in
generation diversity, and the effectiveness with generated progressive samples
are highlighted in downstream classifications. The code is available at
https://github.com/Rowerliu/ADD.
