Generative Deep Learning in Digital Pathology Workflows.

Area of Science:

• Histopathology
• Digital Pathology
• Computer Vision
• Deep Learning
• Generative Models

Background:

• Histopathology labs are increasingly adopting digital workflows.
• Digitized tissue images enable research into automated clinical reporting and diagnosis.
• Computer vision, particularly deep learning, offers potential for accurate pathology identification.

Purpose of the Study:

• To introduce generative models in the context of digital pathology.
• To explore applications of generative models in histopathology.
• To discuss future directions for generative models in this field.

Main Methods:

• Review of current literature on generative models and digital pathology.
• Discussion of deep learning techniques for image analysis.
• Exploration of generative modeling for image artifact removal and data augmentation.

Main Results:

• Generative models can remove artifacts and adapt image domains.
• Synthetic digital tissue samples can be generated using these models.
• High accuracy in identifying pathologies is achievable with deep learning.

Conclusions:

• Generative models offer significant potential for advancing digital pathology.
• Applications include image enhancement, data synthesis, and diagnostic support.
• Future research should focus on further integrating these models into histopathology workflows.