PriorPath: Coarse-To-Fine Approach for Controlled De-Novo Pathology Semantic Masks Generation.

Area of Science:

• Digital pathology
• Computational pathology
• Artificial intelligence in medicine

Background:

• Digital pathology leverages artificial intelligence (AI) for enhanced image analysis and diagnostics.
• Biased datasets due to tissue diversity and labeling limitations hinder AI applicability.
• Existing methods for synthetic histopathological image generation suffer from mode collapse, failing to capture data diversity.

Purpose of the Study:

• To introduce PriorPath, a novel pipeline for generating controllable, realistic semantic masks and synthetic histopathological images.
• To address the limitations of existing generative models in capturing data diversity and controlling cellular characteristics.

Main Methods:

• PriorPath generates detailed semantic masks from coarse-grained tissue images, enabling control over spatial distribution.
• The pipeline uses these masks as priors for conditional generative approaches to create photorealistic synthetic images.
• The method was validated across skin, prostate, and lung cancer datasets.

Main Results:

• PriorPath effectively covers the semantic mask space, outperforming previous methods in similarity to real masks.
• The approach allows for precise control over tissue distributions in generated synthetic images.
• Demonstrated efficacy across three distinct cancer types (skin, prostate, lung).

Conclusions:

• PriorPath offers a state-of-the-art, controllable solution for generating synthetic histopathological images.
• This facilitates the creation of robust, unbiased AI models for computational pathology.
• Enables advancements in clinical decision support, diagnostics, and early cancer detection.