A Morphology Focused Diffusion Probabilistic Model for Synthesis of Histopathology Images

Table of Contents

Title: A Morphology Focused Diffusion Probabilistic Model for Synthesis of Histopathology Images

Authors: Puria Azadi Moghadam, Sanne Van Dalen, Karina C. Martin, Jochen Lennerz, Stephen Yip, Hossein Farahani, Ali Bashashati

Published: Sep 27 2022

Link: https://arxiv.org/abs/2209.13167

Summary (Generated by Microsoft Copilot):

Introduction:

  • The paper explores the use of diffusion probabilistic models to generate synthetic histopathology images, focusing on brain cancer.

Challenges:

  • Histopathology diagnosis is time-consuming and subjective, with limited exposure to rare variants.
  • Generative models in pathology are still in their infancy.

Methods:

  • Utilizes color normalization and perception prioritized weighting to enhance image quality.
  • Compares diffusion models with Generative Adversarial Networks (GANs).

Novelties:

  • First to propose using diffusion probabilistic models for histopathology image synthesis.
  • Introduces morphology prioritization to improve image detail.

Results:

  • Diffusion models outperform GANs in generating high-quality images.
  • Synthetic images are nearly indistinguishable from real ones.

Performances:

  • Achieved better scores in Inception Score (IS), Fréchet Inception Distance (FID), and sFID metrics.
  • Higher precision and recall in image quality and diversity.

Limitations:

  • Longer sampling time compared to GANs due to multiple diffusion steps.

Discussion:

  • The method could be used for educational, privacy, and data augmentation applications.
  • Future work includes optimizing the model to reduce sampling time.