Overcoming Pathology Image Data Deficiency: Generating Images from Pathological Transformation Process

Histopathology serves as the gold standard for medical diagnosis but faces application limitations due to the shortage of medical resources. Leveraging deep learning, computer-aided diagnosis has the potential to alleviate the pathologist scarcity and provide timely clinical analysis. However, developing a reliable model generally necessitates substantial data for training, which is challenging in pathological field. In response, we propose an adaptive depth-controlled bidirectional diffusion (ADBD) network for image data generation. The domain migration approach can work with small trainset and overcome the diffusion overfitting by source information guidance. Specifically, we developed a hybrid attention strategy to blend global and local attention priorities, which guides the bidirectional diffusion and ensures the migration success. In addition, we developed the adaptive depth-controlled strategy to simulate physiological transformations, capable of yielding unlimited cross-domain intermediate images with corresponding soft labels. ADBD is effective for overcoming pathological image data deficiency and supportable for further pathology-related research.