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The paper claims that a single foundation model—LPFM—restores blurry, noisy, low-resolution pathology images and performs virtual staining through one prompt-controlled architecture, reporting better performance than specialized methods on

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A prompt-guided diffusion model pretrained on 190 million pathology patches outperforms task-specific models across most restoration and virtual staining benchmarks.

T0 review reviewed 2026-08-05 challenge →

load-bearing objection Big unified pathology restoration model, but the headline significance claim is undercut by pretraining on internal test patches. the 4 major comments →

arxiv 2509.01071 v1 pith:BZCWZK5T submitted 2025-09-01 cs.CV

A Unified Low-level Foundation Model for Enhancing Pathology Image Quality

Ziyi Liu , Zhe Xu , Jiabo Ma , Wenqaing Li , Junlin Hou , Fuxiang Huang , Xi Wang , Ronald Cheong Kin Chan
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Terence Tsz Wai Wong Hao Chen
This is my paper
classification cs.CV
keywords pathology image restorationvirtual stainingfoundation modelcontrastive learningconditional diffusiontext-prompt guidancewhole slide imageslow-level vision
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

Pathology images frequently arrive blurry, noisy, low-resolution, or with stains that are costly and slow to produce. This paper claims that a single foundation model can handle all of these low-level problems at once, including turning label-free autofluorescence images into H&E stains and converting H&E into PAS-AB or multiplex immunohistochemistry stains. The model combines two stages: a contrastively pre-trained encoder that learns stain-invariant features from 190 million unlabeled pathology patches and produces a coarse restoration, and a conditional diffusion model that refines details under the control of a text prompt. Across 66 tasks spanning restoration and virtual staining, the authors report that LPFM beats specialized methods with statistical significance in 56 of them, with PSNR gains of 10–15% on restoration and SSIM gains of 12–18% on virtual staining. If the results hold in clinical practice, a single system could replace separate denoising, deblurring, super-resolution, and staining tools, lowering cost and turnaround time while making quality enhancement more accessible.

Core claim

The paper's central claim is that low-level pathology image enhancement does not need a collection of task-specific networks. LPFM is a unified architecture trained in two stages: a contrastive pre-training stage learns representations that are invariant to stain and degradation type from 190 million unlabeled patches, producing coarse restored images; a prompt-conditioned latent diffusion stage refines those images using a text prompt such as "obtain high-quality H&E pathology image" or "translate the label-free patch to H&E image." Because tasks are selected by prompt rather than by architecture, the same model performs super-resolution (2x, 4x, 8x), deblurring (Gaussian kernels of size 7–

What carries the argument

The load-bearing mechanism is a two-stage generative pipeline. Stage one is a KL-autoencoder pre-trained with a contrastive loss that pulls together latent features of degraded and high-quality views of the same tissue while pushing apart features of different tissues, plus reconstruction, perceptual, and adversarial losses; this yields a coarse restoration and a degradation-robust, stain-invariant latent space. Stage two is a latent conditional diffusion model whose U-Net receives both the coarse restoration and a pretrained text-prompt embedding as conditions, so a single set of weights switches among restoration and staining tasks. The text prompt is the control surface: changing the prom

Load-bearing premise

The load-bearing premise is that the synthetic degradations used to create training and test pairs—Gaussian blur with kernel sizes 7–15, Gaussian and Poisson noise with sigma 21/31/41, and downscaling by 2/4/8 with area, bilinear, or bicubic interpolation—faithfully represent real artifacts found in clinical whole-slide images.

What would settle it

Collect a set of real clinical whole-slide images with paired low-quality and high-quality acquisitions (for example, the same slides scanned twice, once with defocus or added noise and once at full quality, or same-slide autofluorescence/H&E pairs) and compare LPFM against SwinIR and RegGAN on those real pairs using PSNR and SSIM. If LPFM's advantage over these specialized baselines shrinks to near zero or reverses, the claim that its synthetic degradation pipeline captures clinical artifacts—and hence that the unified model transfers—would be falsified.

Watch this falsifier. Get emailed when new claim-graph text bears on it.

If this is right

  • A hospital could deploy one model instead of separate denoising, deblurring, super-resolution, and virtual-staining systems, with task selection by text prompt.
  • The same pretrained weights can be applied to tissue types and scanners represented in the external sets (TIGER, MIDOG, OCELOT) without retraining, as shown by the external validation tasks.
  • Coupled-degradation restoration (blur plus noise plus downsampling) outperforms specialized single-task models by 2.10 dB PSNR, suggesting combined artifacts can be corrected jointly rather than sequentially.
  • Virtual staining of H&E into PAS-AB and mIHC, and of autofluorescence into H&E, reaches pixel-level and structural metrics above current GAN baselines, which would support stain-free and retrospective-staining workflows if diagnostic concordance is confirmed.
  • Prompt-based control implies output can be steered by user intent (for example, "upscale x2" versus "remove noise"), enabling interactive or prioritized enhancement during review.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • If the synthetic degradation pipeline mirrors real scanner and preparation artifacts, LPFM could be inserted upstream of existing diagnostic foundation models as a pre-processing step; the paper evaluates image-quality metrics, not downstream diagnostic accuracy, so that benefit is an inference.
  • Because task switching happens through prompts, LPFM may adapt to new stain protocols or artifact types zero-shot by editing the prompt; the paper demonstrates prompt-controlled generation but does not test unchanged-weight generalization to unseen stain pairs.
  • The finding that LPFM scores higher PSNR on degraded HEMIT inputs than on high-quality ones (26.99 versus 26.49 dB) suggests the model may actively exploit artifact patterns during stain conversion; if confirmed, it argues for jointly optimizing restoration and translation rather than separating them.
  • A practical extension would be a controlled clinical study comparing pathologist diagnostic concordance on LPFM-enhanced and physically restained slides; such a study would test whether the reported metric gains translate into decisions.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit.

Referee Report

4 major / 6 minor

Summary. The paper proposes LPFM, a unified low-level pathology foundation model intended to handle both image restoration (super-resolution, deblurring, denoising, coupled degradations) and image translation (virtual staining) within a single architecture. The method combines contrastive pretraining on roughly 190 million patches from 87,810 WSIs with a prompt-conditioned latent diffusion refinement stage. The authors report results on 66 tasks with 95% confidence intervals and claim statistically significant superiority (p<0.01) over eight baselines in 56/66 tasks. Three of the benchmark datasets (MIDOG2022, TIGER2021, OCELOT) are explicitly excluded from pretraining for external validation. The manuscript is well organized and the scale of the curated dataset is notable, but the experimental section contains a serious internal-validity problem: the internal test sets are also present in the contrastive pretraining corpus. In addition, the statistical test underlying the headline significance claim is never described, and all degraded/restoration benchmarks are generated from the authors' own synthetic degradation pipeline.

Significance. If the reported effects are real, LPFM would be a meaningful step toward unified low-level vision in computational pathology: the combination of contrastive pretraining with prompt-conditioned diffusion is sensible, and the decision to hold out three datasets from pretraining is a genuine strength. The scale of data curation and the breadth of tasks also add value. However, the conclusions currently rest on comparisons whose internal test portions are confounded by pretraining data overlap, and on a significance claim with no described test. The external datasets provide a cleaner test bed, but the aggregate 56/66 claim mixes contaminated and uncontaminated tasks. The paper's significance is therefore conditional on repairing the experimental protocol and reporting external/clean results separately.

major comments (4)
  1. [Sec. 2.1 / Sec. 4.5] The internal evaluation is confounded by pretraining data overlap. Sec. 4.5 states the contrastive pretraining corpus includes CAMELYON16, PANDA, PAIP2020, AF2HE, PASAB, and HEMIT, while Sec. 2.1 uses the first three as 'internal' benchmarks with train/val/test splits. The test patches from these datasets were therefore seen by the LPFM encoder during contrastive pretraining. The baselines (CycleGAN, SwinIR, etc.) are trained from scratch on the downstream training split only. The headline claim of 56/66 statistically significant improvements includes these contaminated internal tasks. Please re-run with the internal test sets excluded from pretraining, and separately report results for the clean external sets (MIDOG2022, TIGER2021, OCELOT).
  2. [Abstract / Sec. 2] No statistical test is specified for the p<0.01 / 56/66 claim. The text and figures report p-values (e.g., Fig. 2-5) and mention 'significance testing' and 95% CIs, but no test name, null hypothesis, sample unit, or multiple-comparison correction is given. With 66 tasks and 3 metrics, uncorrected pairwise tests would make many 'significant' findings expected by chance. Please specify the test, the multiplicity adjustment, and provide a per-task significance table with internal/external breakdown.
  3. [Sec. 4.1.2 / Sec. 3] All restoration and degraded-staining evaluations are based on the authors' synthetic degradation pipeline (Gaussian blur kernels 7-15, Gaussian/Poisson noise σ=21/31/41, downscaling 2/4/8 by area/bilinear/bicubic). The 'external' datasets test tissue/scanner/stain transfer under this same pipeline, not generalization to real scanner artifacts or preparation defects. The Discussion's clinical claims (e.g., 'transform diagnostic workflows') require support from real or independently acquired degraded pathology images. At minimum, validate on a real degraded dataset or substantially soften the generality claims.
  4. [Tables 1-3, 6, 11-12] The extended-data tables contain implausible repetitions that undermine confidence in the reported rankings. Tables 1-3 list identical Pix2Pix rows (same PSNR/SSIM/LPIPS and CIs) for CAMELYON16, PAIP2020, and PANDA at every super-resolution scale. Table 6 shows the OCELOT ×4 LDM row identical to the BSRGAN row of Table 4. Tables 11-12 have repeated or mislabeled kernel-size rows (15, 11, 15 and 15, 7, 15 instead of 7/11/15). These must be corrected and regenerated from the actual experiments before the extended-data results can support the aggregate rankings.
minor comments (6)
  1. [Abstract / Fig. 1] Typographical issues: 'whole slied images' in the abstract; 'virutal' in Fig. 1; '0=cs' in Fig. 13 caption. These should be corrected.
  2. [Eq. (7)] The equation uses x_d but the surrounding text defines 'xp' as the paired degraded/source-stained image. Please define the variable consistently.
  3. [Eq. (15) / Sec. 4.3] The text states 'A lower MAE indicates better pixel-wise accuracy' immediately after defining MSE. MAE is not defined; this appears to be a typo for MSE or should be a separate metric.
  4. [Fig. 12 / Fig. 13] These figures contain Chinese-language placeholder text (e.g., '很丑，要换的一个过程') and random filename strings. These should be removed before submission.
  5. [Sec. 2.6] The sentence 'LPFM showed merely 6.2' is incomplete, breaking the logic of the robustness comparison. Please complete or rephrase.
  6. [Table 27 / Sec. 1] The paper claims '34 tissue types' but Table 27 lists 40 primary-site rows, including 'Pancancer' and 'Prostate,' which are not tissue types. Please reconcile the count.

Circularity Check

1 steps flagged

Internal 'held-out' test patches were included in contrastive pretraining, so the 56/66 significance claim is partially circular; external validation remains independent.

specific steps
  1. fitted input called prediction [Sec 4.5 (Datasets) and Sec 2.1 (Super Resolution), also affecting internal tasks in Secs 2.2-2.6 and the abstract's 56/66 claim]
    "Our unified low-level pathology foundation model is pretrained on all the datasets excluding MIDOG2022, TIGER2021, and OCELOT which are reserved for external validation. ... For internal validation, we employed three benchmark datasets (CAMELYON16, PANDA, and PAIP2020), which were rigorously partitioned into training (70%), validation (10%), and test (20%) sets with no data overlap to ensure unbiased evaluation."

    Section 4.5 places CAMELYON16, PANDA, and PAIP2020 inside the pretraining corpus: they are part of 'all the datasets' from which 190M patches are drawn. Section 2.1 then uses these same three datasets to define internal 'held-out' test sets. Because LPFM's contrastive encoder was pretrained on the exact test patches, the internal evaluation is not held out: the model has already seen the test images. Calling these results 'predictions' on held-out data is therefore incorrect by the paper's own dataset construction. The headline aggregate '56/66 tasks with p<0.01' counts these contaminated internal tasks together with truly external ones, so a portion of the central claim reduces to pretraining exposure rather than independent generalization.

full rationale

The core architecture and losses (contrastive pretraining, conditional diffusion, prompt conditioning) are not self-referential, and no parameter is fitted to test labels. The external datasets (MIDOG2022, TIGER2021, OCELOT) are explicitly excluded from pretraining and provide genuinely independent evidence of generalization. However, the internal benchmark is circular in an evaluation sense: the pretraining corpus includes all slides from CAMELYON16, PANDA, and PAIP2020, which are later 'rigorously partitioned' into train/val/test. LPFM's contrastive encoder has therefore already seen the exact internal test patches, while baselines trained only on the downstream train split have not. This gives LPFM an unfair advantage on the 36 internal tasks and confounds the aggregated 56/66 significance claim. The paper does not provide a per-task internal/external breakdown of the p<0.01 count, so the strength of the headline claim cannot be assessed from the reported numbers. This is a partial circularity in the evaluation protocol, not a derivation-level circularity: the external results and the virtual-staining results on datasets not in the pretraining corpus still carry independent weight. Score 6 reflects that a substantial portion of the central empirical claim is contaminated by the train/test overlap, while acknowledging the existence of independent external support.

Axiom & Free-Parameter Ledger

4 free parameters · 4 axioms · 0 invented entities

The paper's central claims rest on standard generative-model machinery plus domain assumptions about degradation realism and stain-invariant representation learning. No new physical entities are introduced. The primary free parameters are the degradation simulation choices and the prompt templates, which define the benchmark but are not fitted to external data.

free parameters (4)
  • Degradation simulation parameters = blur kernel 7-15, sigma1/sigma2 1.5-3.5; noise sigma 21/31/41; SR scales 2/4/8
    Defined by the authors to simulate clinical conditions (Sec. 4.1.2). They define the evaluation space, and the model's reported performance is specific to these choices.
  • Coupled degradation composition = Randomized combinations, exact distributions not specified
    In Sec. 2.5, coupled degradations use random blur, Poisson noise, and low resolution, but the sampling distribution is not defined, making the benchmark setup a free choice.
  • Prompt templates = Hand-written prompts such as 'Obtain the high-quality H&E pathology image'
    The text prompts are authored by the researchers (Fig. 10-11). Model behavior depends on these exact strings, which are not systematically swept.
  • Training hyperparameters = Not reported
    Loss weights, contrastive temperature tau, diffusion timesteps, learning rates, and batch sizes are not given in the manuscript, leaving key training choices unspecified.
axioms (4)
  • standard math Standard diffusion and latent diffusion model mathematics (Eqs. 11-14)
    The paper invokes LDM and DDIM without proof, which is acceptable as standard background.
  • domain assumption Synthetic degradation models are representative of clinical artifacts
    Sec. 4.1.2 assumes Gaussian blur, Poisson/Gaussian noise, and downscaling capture real scanner and preparation artifacts. All results depend on this.
  • domain assumption Contrastive pretraining on public pathology datasets yields stain-invariant features
    Sec. 4.2 argues that contrastive learning on 190M patches transfers across tissue types and staining protocols. This is plausible but not independently validated here.
  • ad hoc to paper Hand-crafted prompts sufficiently specify each task
    The prompt strings used to condition the diffusion model are chosen by the authors and not justified beyond intuition (Fig. 10-11).

reviewed 2026-08-05 · how reviews work

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Cite this review

Pith. "Pith review of A Unified Low-level Foundation Model for Enhancing Pathology Image Quality." pith.science (2026). https://pith.science/paper/BZCWZK5T

@misc{pith2026250901071,
  author       = {Pith},
  title        = {Pith review of: A Unified Low-level Foundation Model for Enhancing Pathology Image Quality},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/BZCWZK5T}},
  note         = {Machine review of arXiv:2509.01071}
}
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read the original abstract

Foundation models have revolutionized computational pathology by achieving remarkable success in high-level diagnostic tasks, yet the critical challenge of low-level image enhancement remains largely unaddressed. Real-world pathology images frequently suffer from degradations such as noise, blur, and low resolution due to slide preparation artifacts, staining variability, and imaging constraints, while the reliance on physical staining introduces significant costs, delays, and inconsistency. Although existing methods target individual problems like denoising or super-resolution, their task-specific designs lack the versatility to handle the diverse low-level vision challenges encountered in practice. To bridge this gap, we propose the first unified Low-level Pathology Foundation Model (LPFM), capable of enhancing image quality in restoration tasks, including super-resolution, deblurring, and denoising, as well as facilitating image translation tasks like virtual staining (H&E and special stains), all through a single adaptable architecture. Our approach introduces a contrastive pre-trained encoder that learns transferable, stain-invariant feature representations from 190 million unlabeled pathology images, enabling robust identification of degradation patterns. A unified conditional diffusion process dynamically adapts to specific tasks via textual prompts, ensuring precise control over output quality. Trained on a curated dataset of 87,810 whole slied images (WSIs) across 34 tissue types and 5 staining protocols, LPFM demonstrates statistically significant improvements (p<0.01) over state-of-the-art methods in most tasks (56/66), achieving Peak Signal-to-Noise Ratio (PSNR) gains of 10-15% for image restoration and Structural Similarity Index Measure (SSIM) improvements of 12-18% for virtual staining.

Figures

Figures reproduced from arXiv: 2509.01071 by Fuxiang Huang, Hao Chen, Jiabo Ma, Junlin Hou, Ronald Cheong Kin Chan, Terence Tsz Wai Wong, Wenqaing Li, Xi Wang, Zhe Xu, Ziyi Liu.

Figure 1
Figure 1. Figure 1: Overview of the proposed low-level pathology foundation model (LPFM) for pathology image restoration and virutal staining. a. The pathology imaging and virtual staining workflow, illustrating the process from tissue sampling to pathology images. LPFM can achieve pathology image restoration and virtual staining in the workflow. b. The unified architecture of LPFM integrates contrastive pre-training and prom… view at source ↗
Figure 2
Figure 2. Figure 2: Results of pathology image super resolution tasks. a. Average ranking of LPFM and compared methods based on PSNR, SSIM and LPIPS across 18 super resolution tasks. b-d. Average PSNR, SSIM and LPIPS of LPFM and compared methods across 18 super resolution tasks. Error bars represent 95% CI. The box limits represent the standard error. e-f. Intensity profiles along the dashed yellow and red lines for the groun… view at source ↗
Figure 3
Figure 3. Figure 3: Results of pathology image deblurring tasks. a. Average ranking of LPFM and compared methods based on PSNR, SSIM and LPIPS across 18 deblurring tasks. b-d. Average PSNR, SSIM and LPIPS of LPFM and compared methods across 18 deblurring tasks. Error bars represent 95% CI. The box limits represent the standard error. e-f. Intensity profiles along the dashed yellow and red lines for the GT image and the top fo… view at source ↗
Figure 4
Figure 4. Figure 4: Results of pathology image denoising tasks. a. Average ranking of LPFM and compared methods based on PSNR, SSIM and LPIPS across 18 denoising tasks. b-d. Average PSNR, SSIM and LPIPS of LPFM and compared methods across 18 denoising tasks. Error bars represent 95% CI. The box limits represent the standard error. e-f. Intensity profiles along the dashed yellow and green lines for the GT image and the top fou… view at source ↗
Figure 5
Figure 5. Figure 5: Results of pathology image virtual staining tasks. a-c. Average PSNR of LPFM and compared methods based on AF2HE, HE2PAS and HEMIT datasets. If LPFM outperforms the second-best method, the p-value is also reported. The box limits represent the standard deviation, and the error bars indicate the 2.5% and 97.5% percentiles. d. The original AF image, chemically H&E-stained image, and virtually stained images … view at source ↗
Figure 6
Figure 6. Figure 6: Restoration results of pathology images with coupled degradations. a-c. Average PSNR, SSIM and LPIPS of LPFM and compared methods on CAMELYON16, PAIP2020, PANDA, MIDOG2022, TIGER2021 and OCELOT datasets. d-e. The high-quality ground truth pathology images, degraded low-quality pathology images, mean average error images and restored pathology images generated by various methods on internal CAMELYON16 and e… view at source ↗
Figure 7
Figure 7. Figure 7: Virtual staining results of pathology images with coupled degradations. a-b. The high-quality H&E images, degraded H&E images, PAS ground truth (GT) images and virtually stained PAS pathology images generated by various methods on HE2PAS dataset. c-d. The high-quality H&E images, degraded H&E images, mIHC ground truth (GT) images and virtually stained mIHC pathology images generated by various methods on H… view at source ↗
Figure 8
Figure 8. Figure 8: The pipeline of unified low-level pathology foundation model in the training stage. a. We propose a pretraining framework that learns degradation-robust representation through contrastive learning and pixel-wise reconstruction, enabling coarse restoration of images with coupled degradations. b. We propose a conditional diffusion model that improves image quality through a guided denoising process, utilizin… view at source ↗
Figure 9
Figure 9. Figure 9: The inference pipeline of unified low-level pathology foundation model for image restoration and virtual staining. 24/49 [PITH_FULL_IMAGE:figures/full_fig_p024_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: H&E pathology image restoration examples with varying textual prompt guidance. Virtual Staining Translate the H&E image to PAS-AB image Positive: PAS-AB image Negative: H&E image, blurry, noisy, low-qualityunsharp Virtual Staining Translate the H&E image to mIHC image Positive: mIHC image Negative: H&E image, blurry, noisy, low-qualityunsharp Virtual Staining Translate the H&E image to Autofluorescence im… view at source ↗
Figure 11
Figure 11. Figure 11: Pathology image virtually staining examples with varying textual prompt guidance. 25/49 [PITH_FULL_IMAGE:figures/full_fig_p025_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: Effectiveness of autoencoder pretraining via contrastive learning (CL) for pathology image restoration and virtually staining. a-c. The high-quality ground truth pathology images, degraded low-quality pathology images (a. low resolution, b. noise, c. blur), mean average error (MAE) images and restored pathology images generated by LPFM with or withour CL. Results of PSNR, SSIM and LPIPS of LPFM with or wi… view at source ↗
Figure 13
Figure 13. Figure 13: Effectiveness of image refinement (RF) via conditional diffusion model for pathology image restoration and virtually staining. a-c. The high-quality ground truth pathology images, degraded low-quality pathology images (a. low resolution, b. noise, c. blur), mean average error (MAE) images and restored pathology images generated by LPFM with or withour RF. Results of PSNR, SSIM and LPIPS of LPFM with or wi… view at source ↗
Figure 14
Figure 14. Figure 14: More samples of different methods for pathology image super resolution tasks. The original GT images, 8 times downsampled images (LR 8) and restored images generated by various methods on internal and external datasets. 28/49 [PITH_FULL_IMAGE:figures/full_fig_p028_14.png] view at source ↗
Figure 15
Figure 15. Figure 15: More samples of different methods for pathology image deblurring tasks. The original GT images, blurry images with 15 pixel Gaussian kernel (Blur 15) and restored images generated by various methods on internal and external datasets. 29/49 [PITH_FULL_IMAGE:figures/full_fig_p029_15.png] view at source ↗
Figure 16
Figure 16. Figure 16: More samples of different methods for pathology image denoising tasks. The original GT images, noisy images with additive Gaussian noise (σ=41) (Noise 41) and restored images generated by various methods on internal and external datasets. 30/49 [PITH_FULL_IMAGE:figures/full_fig_p030_16.png] view at source ↗
Figure 17
Figure 17. Figure 17: Extended qualitative results showcasing the restoration performance of different methods on pathology images with coupled degradations. 31/49 [PITH_FULL_IMAGE:figures/full_fig_p031_17.png] view at source ↗
Figure 18
Figure 18. Figure 18: More samples of different methods for virtual staining on AF2HE dataset (autofluorescence to H&E stain). The autofluorescence images, GT (H&E) images and virtually stained images generated by various methods are presented. 32/49 [PITH_FULL_IMAGE:figures/full_fig_p032_18.png] view at source ↗
Figure 19
Figure 19. Figure 19: More samples of different methods for virtual staining on HE2PAS dataset, including the paired H&E stained and Periodic Acid-Schiff-Alcian Blue (PAS-AB) stained images. The H&E images, GT (PAS-AB) images and virtually stained images generated by various methods are presented. 33/49 [PITH_FULL_IMAGE:figures/full_fig_p033_19.png] view at source ↗
Figure 20
Figure 20. Figure 20: More samples of different methods for virtual staining on HEMIT dataset, including the paired H&E stained and multiplex immunohistochemistry (mIHC) stained images. The H&E images, GT (mIHC) images and virtually stained images generated by various methods are presented. 34/49 [PITH_FULL_IMAGE:figures/full_fig_p034_20.png] view at source ↗

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This paper was first reviewed by deepseek-v4-flash on August 5, 2026.