Stain Transformation

Stain transformation in digital pathology aims to computationally convert histological images from one stain type (e.g., H&E) to another (e.g., IHC), overcoming limitations of traditional staining methods. Current research heavily utilizes generative adversarial networks (GANs), particularly CycleGANs and their variations, along with diffusion models, to achieve this translation, often incorporating techniques like knowledge distillation and self-supervision to improve accuracy and structural preservation. This field is significant because it promises faster, cheaper, and more accessible diagnostic tools in pathology, potentially improving healthcare by enabling wider access to specialized stains and reducing the need for extensive and costly laboratory procedures.