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Fixation and Sectioning

Fixation and Sectioning

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Related Experiment Video

Updated: Jun 25, 2025

Image Processing Protocol for the Analysis of the Diffusion and Cluster Size of Membrane Receptors by Fluorescence Microscopy

Image Processing Protocol for the Analysis of the Diffusion and Cluster Size of Membrane Receptors by Fluorescence Microscopy

Published on: April 9, 2019

PathLDM: Text conditioned Latent Diffusion Model for Histopathology.

Srikar Yellapragada¹, Alexandros Graikos¹, Prateek Prasanna¹

¹Stony Brook University.

IEEE Winter Conference on Applications of Computer Vision. IEEE Winter Conference on Applications of Computer Vision

|May 29, 2024

Summary

This summary is machine-generated.

Pathology Latent Diffusion Model (PathLDM) generates high-quality histopathology images using text reports. This approach enhances data-efficient training for specialized AI models in computational pathology.

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Area of Science:

Artificial Intelligence
Medical Imaging
Computational Pathology

Background:

High-quality diffusion models require extensive datasets, posing challenges for specialized fields like computational pathology.
Histopathology reports offer rich clinical data, making them valuable for guiding generative models in this domain.

Purpose of the Study:

Introduce PathLDM, the first text-conditioned Latent Diffusion Model for generating high-quality histopathology images.
Enhance generative model training in computational pathology through data-efficient, text-guided image synthesis.

Main Methods:

Developed PathLDM, a novel text-conditioned Latent Diffusion Model.
Fused image and textual data from histopathology reports for enhanced generation.
Utilized GPT for distilling and summarizing complex pathology reports to create an effective conditioning mechanism.

Main Results:

Achieved a State-of-the-Art (SoTA) FID score of 7.64 for text-to-image generation on the TCGA-BRCA dataset.
Significantly outperformed the closest text-conditioned competitor (FID 30.1).
Demonstrated PathLDM's capability in generating high-quality histopathology images guided by textual data.

Conclusions:

PathLDM represents a significant advancement in text-to-image generation for histopathology.
The model's text-conditioning approach enables data-efficient training and high-fidelity image synthesis.
This work paves the way for improved AI tools in computational pathology, leveraging clinical text data.