The field of digital pathology is moving towards the development of more accurate and reliable computational models for image analysis and registration. Recent studies have highlighted the limitations of current AI pipelines in replicating expert-level grading, emphasizing the need for modular evaluation and standardized computational frameworks. Notably, innovative approaches to stain translation and image registration have shown promise in improving image quality and fidelity.

Particularly noteworthy papers include:

• Progressive Translation of H&E to IHC with Enhanced Structural Fidelity, which proposes a novel network architecture for stain translation that preserves structural authenticity and color fidelity.
• Building Trust in Virtual Immunohistochemistry: Automated Assessment of Image Quality, which introduces an automated framework for evaluating the accuracy of virtual IHC stains.