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A Morphology Focused Diffusion Probabilistic Model for Synthesis of Histopathology Images
Puria Azadi Moghadam, Sanne Van Dalen, Karina C. Martin, Jochen Lennerz, Stephen Yip, Hossein Farahani, Ali Bashashati
TL;DR
Histopathology image generation has received limited study despite potential uses for education, proficiency testing, privacy, and data augmentation. This paper combines diffusion models with morphology-focused weighting and color normalization to synthesize histopathology images, and reports better performance than ProGAN alongside a survey in which pathologists could not distinguish synthetic from real images.
Problem
Although deep learning has advanced histopathology analysis, its use for generating histopathology images has received limited study despite potential educational, privacy, and data-augmentation uses.
Source: §1 · p. 2Method
The paper uses diffusion probabilistic models with perception-prioritized weighting and color normalization to synthesize morphology-focused histopathology images.
Source: §1 · p. 2, §3.2 · p. 3Results
Takeaways & Limitations
The authors conclude that the method could facilitate synthesized histology images for education, privacy, and data augmentation.
Source: §5 · p. 8Diffusion sampling takes relatively longer than GAN sampling because it uses multiple small diffusion steps.
Source: §5 · p. 8
Abstract
from arXiv · showhide
Visual microscopic study of diseased tissue by pathologists has been the cornerstone for cancer diagnosis and prognostication for more than a century. Recently, deep learning methods have made significant advances in the analysis and classification of tissue images. However, there has been limited work on the utility of such models in generating histopathology images. These synthetic images have several applications in pathology including utilities in education, proficiency testing, privacy, and data sharing. Recently, diffusion probabilistic models were introduced to generate high quality images. Here, for the first time, we investigate the potential use of such models along with prioritized morphology weighting and color normalization to synthesize high quality histopathology images of brain cancer. Our detailed results show that diffusion probabilistic models are capable of synthesizing a wide range of histopathology images and have superior performance compared to generative adversarial networks.
1. Introduction
Deep learning has primarily advanced histopathology classification and related analysis, while generative image synthesis remains less explored despite potential uses for rare subtypes, education, privacy, and data augmentation. This paper explores diffusion models for histopathology synthesis, using morphology-focused weighting and color normalization, and reports better performance than a GAN baseline.
1. Introduction
- Most deep-learning work in histopathology has focused on discriminative tasks such as classification, segmentation, and outcome prediction.The introduction contrasts this emphasis with the comparatively limited use of generative models in pathology.Source: §1 · p. 2
- Synthetic histology images may support training on rare cancer subtypes, proficiency testing, privacy-preserving sharing, and dataset augmentation.The paper notes that labeled medical images can be costly to obtain, which can contribute to model overfitting.Source: §1 · p. 2
- The paper investigates diffusion probabilistic models for synthesizing histopathology images and comparing image quality and artefacts with state-of-the-art methods.The authors describe this as the first use of diffusion models for synthetic histopathology image generation.Source: §1 · p. 2
- Color normalization and perception-prioritized weighting are used to direct learning toward morphological patterns and structurally important diffusion stages.Perception-prioritized weighting (P2) prioritizes diffusion stages containing more important structural histopathology content.Source: §1 · p. 2
- The proposed diffusion method outperforms the GAN-based comparison across all reported metrics and produces images close to ground truth.The study uses a low-grade glioma dataset and evaluates generated-image quality with multiple objective and subjective metrics.Source: §1 · p. 2
2. Related Works
Prior histopathology image-generation work has included GAN-based approaches, while diffusion models have been applied in other domains. The paper motivates diffusion for histopathology synthesis by its potential stability and distribution coverage, noting that this application had not yet been explored.
2. Related Works
- Prior work includes generative approaches for tumor proportion scoring, image augmentation, and high-resolution histopathology synthesis, including Progressive GANs.The cited examples include a U-net GAN and Progressive GAN models.Source: §2 · p. 2
- GANs can suffer from mode collapse, training instability, and discriminator overfitting, making rare-condition or imbalanced-data generation challenging.The paper links these drawbacks to producing images directly from complex latent spaces in a single step.Source: §2 · p. 2
- Diffusion models have been used for tasks such as image segmentation, text-to-speech, and handwriting generation, but not yet for synthetic histopathology images.The paper hypothesizes that diffusion could generate high-quality histopathology images and address some challenges of GAN-based models.Source: §2 · pp. 2–3
- Diffusion models use multiple denoising steps and conditioning, which the paper associates with greater stability, diversity, distribution coverage, and scalability.The authors connect these potential advantages to histopathology datasets that include rare subtypes and class imbalance.Source: §2 · p. 3
3.1. Problem Definition
The paper frames histopathology synthesis as generating images represented by morphological and genomic features, with low-grade glioma as an application. Its setup treats genotypes as categories for which image collections are considered.
3.1. Problem Definition
- The objective is to generate histopathology images represented by morphological and genomic features, a task described as challenging relative to typical image synthesis.Source: §3.1 · p. 3
- The setup indexes the available natural genotypes and considers an image collection for each genotype.Source: §3.1 · p. 3
- For low-grade glioma, diagnosis combines histopathologic features with genotype features, including IDH and 1p19q codeletion status.Source: §3.1 · p. 3
3.2. Color normalization
Because H&E staining varies across protocols and scanning platforms, the method normalizes color to encourage learning of morphology rather than stain differences. It uses a structure-preserving scheme that transfers images to a target color domain while retaining their stain concentration.
3.2. Color normalization
- Variations in staining protocols and digital scanning platforms make H&E color inconsistent across images.Source: §3.2 · p. 3
- The method converts input images to a shared color domain so the diffusion model focuses on morphology, including cell shape, density, and distribution, rather than stain variation.Source: §3.2 · p. 3
- The structure-preserving Vahadane scheme transfers source images to a target domain while preserving their stain concentration.Source: §3.2 · p. 3
3.3. Diffusion Probabilistic Model
The model gradually adds Gaussian noise to real images, then learns a reverse process that retrieves data from noise. Its training loss and morphology prioritization weight denoising across time steps, while a multiresolution U-Net-based architecture models features at different scales.
3.3.1 Forward Diffusion
- Forward diffusion derives noisy images from real inputs at selected time steps using fixed noise scales and Gaussian noise.The passage describes latent images as derived directly from the real input data.Source: §3.3.1 · p. 4
3.3.2 Parametrized Reverse Diffusion
- Reverse diffusion trains a neural network to denoise each input, iteratively generating images from a roughly isotropic Gaussian latent.The paper compares the learned reverse process to a VAE decoder and the fixed forward process to its encoder.Source: §3.3.2 · p. 4
3.3.3 Training Loss
- The training objective combines a simple noise-prediction loss with a variational-bound term; the authors report that a small c can improve model capacity.The simple loss is an MSE between actual and estimated noise.Source: §3.3.3 · p. 4
3.3.4 Morphology Levels Prioritization
- Morphology prioritization weights earlier time steps more heavily to emphasize perceptual content before later denoising focuses on minor details.The authors report empirically that perception-prioritized weighting can produce higher-detail histopathology images.Source: §3.3.4 · p. 5
3.3.5 The Architecture
- The U-Net-based architecture uses attention at three resolutions to model small cellular features and larger tissue structures.BIGGAN residual blocks are used to help avoid checkerboard artifacts and aliasing; genotype and timestep information enter through separate embeddings.Source: §3.3.5 · p. 5
4. Experimental Evaluation and Results
The evaluation compares diffusion-generated LGG histopathology patches with ProGAN using objective metrics and pathologist ratings. It covers three genomic subtypes and includes visual inspection of generated samples.
4.1. Data
- The dataset comprises 344 TCGA LGG slides across IDHC, IDHNC, and IDHWT, with pathologist-annotated tumor areas tiled into 33,777 patches.Patches were scaled to 128×128 pixels at 10× magnification for training.Source: §4.1 · p. 5
4.2. Experiments
- The comparison used similarly normalized patches, trained both models on all available patches, and sampled 50,000 images from each for metric evaluation.The experiment assessed generated-image quality against ProGAN, which had previously outperformed several other generative models in histology image generation.Source: §4.2.1 · p. 6
4.2.1 Experiment I
- The proposed diffusion model outperformed the state of the art across the reported Inception Score, FID, and sFID metrics, and produced better images than the state of the art in diversity and fidelity.Source: §4.2.1 · p. 7, Table 2 · p. 7
4.2.2 Experiment II
- Pathologists showed no statistically significant difference in their performance identifying real versus synthetic images.The survey included two pathologists and reported its summary in Table 4 and Fisher-test results in Table 5.Source: §4.2.2 · p. 7, Table 5 · p. 8, Table 4 · p. 8
4.3. Visual Observation:
- Generated patches show H&E-like staining and recognized oligodendroglioma features, while ProGAN samples show artifacts for the rarer IDHWT subtype.The authors report that diffusion learned rare class-specific features, whereas ProGAN's IDHWT samples were lower quality and fairly similar to one another.Source: §4.3 · p. 8
5. Conclusion and Future Work
The paper presents an end-to-end diffusion method for H&E histopathology synthesis and reports that it outperforms proGAN, with pathologists unable to distinguish synthetic from real images in a survey. The authors identify longer sampling time than GANs as a future optimization target.
Conclusion and Future Work
- The diffusion approach outperforms proGAN on multiple objective and subjective metrics, and pathologists in a survey could not distinguish synthetic from real images.The authors state that the results suggest the generated images are close to ground truth.Source: §5 · p. 8
- The proposed end-to-end method generates H&E-stained histopathology images and is presented as the first use of diffusion probabilistic models for this task.Source: §5 · p. 8
- Synthesized histology images could support educational, privacy, and data-augmentation applications.Source: §5 · p. 8
- Diffusion sampling takes relatively longer than GAN sampling because it uses multiple small diffusion steps, motivating future work to reduce sampling time.The authors cite Xiao et al. as an example of work that can significantly reduce the number of diffusion steps.Source: §5 · p. 8
6. Supplementary Materials
The supplementary materials provide information omitted from the paper due to space limitations, including forward-diffusion details, implementation parameters, example patches and generated images, and survey visualizations.
6.1. Forward Diffusion in Detail
- Forward diffusion q is a Markov chain producing latents x1,gm through xT,gm; the supplement also gives a reparameterization from x0,gm and defines fixed noise scales.It specifies ε ∼ N(0,I).Source: §6.1 · p. 11
6.2. Model Implementation and Training
- The model was trained in PyTorch for 1000 diffusion steps on an NVIDIA V100 GPU, with remaining parameters listed in Table 6.Source: §6.2 · p. 11, Table 6 · p. 11
6.3. Samples of Real Patches
- Figure 8 shows real 128×128-pixel patches extracted from whole-slide images after color normalization, with each row representing a different genotype.Source: §6.3 · p. 12, Figure 8 · p. 12
6.4. Samples of Generated Images by Our Diffusion Probabilistic Model
- Figure 9 presents the top 10 synthetic images produced by the diffusion model.Source: §6.4 · p. 12, Figure 9 · p. 12
6.5. Survey Visualization
- Figures 10 and 11 show the pathologists’ survey form and individual opinions, with colors distinguishing synthetic and real images, misclassifications, and medium-confidence answers.Brown marks synthetic images classified as real, red marks real images identified as synthetic, and purple marks medium-confidence answers.Source: §6.5 · p. 13, Figure 10 · p. 13, Figure 11 · p. 14