Generating Progressive Images from Pathological Transitions via Diffusion Model
This work addresses data scarcity in computer-aided pathological diagnosis, though it appears incremental as it builds on existing diffusion models with domain migration techniques.
The paper tackles the problem of limited annotated pathological image data by proposing an adaptive depth-controlled diffusion (ADD) network that generates progressive images between different pathological stages for data augmentation, achieving significant improvements in generation diversity and downstream classification performance using tiny training sets (less than 500 samples).
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.