Unpaired Modality Translation for Pseudo Labeling of Histology Images

Armand Collin; Arthur Boschet; Julien Cohen-Adad; Nishka Katoch

REVIEW 3 major objections 4 minor 20 references

Unpaired Modality Translation for Pseudo Labeling of Histology Images

T0 review · 3 major / 4 minor · reviewed 2026-08-11 · deepseek-v4-flash

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Pith's one-line read The paper establishes that translating labeled TEM histology images into SEM style and training a segmentation model on the synthetic images yields pseudo labels with mean Dice $0.736 \pm 0.005$, a usable starting point for manual…

desk verdict Useful practical pseudo-labeling pipeline with an untested checkpoint-selection assumption and a recommendation that contradicts its own SEM results. read the letter →

arxiv 2412.02858 v1 pith:BRJ5JZOX submitted 2024-12-03 eess.IV cs.AIcs.CV

Arthur Boschet , Armand Collin , Nishka Katoch , Julien Cohen-Adad This is my paper · ORCID

classification eess.IVcs.AIcs.CV

keywords pseudolabelingunsupervisedimagetranslationhistologysegmentationaxonandmyelincycle-consistentdiffusionscanningelectronmicroscopytransmissionnnU-Net

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

Histology segmentation is bottlenecked by expert annotation, and annotations made in one imaging modality such as transmission electron microscopy (TEM) do not transfer to another such as scanning electron microscopy (SEM). This paper proposes to bridge the gap with unsupervised image translation: translate labeled images into the target modality, keep the old annotations on the translated images, and train a segmentation model on the synthetic pairs. The headline result is that this tutoring path produces pseudo labels on a real SEM dataset with a mean Dice score of $0.736 \pm 0.005$ for axons and $0.652 \pm 0.005$ for myelin. If correct, the method would let researchers obtain initial segmentations for a new modality with zero target-domain annotations, reducing the manual refinement burden.

What carries the argument

The central mechanism is a cycle-consistent adversarial diffusion translation model (SynDiff) paired with an automatic segmentation framework (nnU-Net). SynDiff learns unpaired translations between the labeled domain $L$ and the unlabeled domain $U$ in both directions: a pair of non-diffusive GANs produce initial coarse translations, and denoising diffusion GANs refine them in fewer than eight timesteps, with a cycle-consistency loss forcing $L \to U \to L$ and $U \to L \to U$ to reconstruct the original images. The tutoring path takes the $L \to U$ translation, recycles the original annotations as if they labeled the synthetic images, trains nnU-Net on those synthetic pairs, and applies it to real $U$ images. The adaptive path takes the $U \to L$ translation and runs a model pre-trained on $L$ on the translated images. What carries the argument is that cycle consistency is assumed to preserve the geometry and boundaries of axons and myelin, so labels remain meaningful across a modality change.

What would settle it

Take a set of real SEM images with expert axon and myelin annotations, run the tutoring path using checkpoints selected by cycle-reconstruction SSIM, PSNR, and L1 exactly as the paper describes, and compare the pseudo labels to the expert masks on the same images. If the Dice score does not replicate near $0.736$ for axons, or if errors concentrate at myelin boundaries, the claim that cycle-consistent translation preserves geometry for labeling is weakened. A sharper variant is to re-select the translation checkpoint by downstream segmentation Dice instead of image-quality metrics; if Dice changes materially, the paper's checkpoint rule is not the one that best serves pseudo labeling.

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Extended reading notes

Core claim

The paper claims that unpaired modality translation can generate usable pseudo labels for histology segmentation in a target domain that has no annotations. Using a labeled TEM dataset as the source, it translates images into three increasingly different target domains (TEM-MACAQUE, SEM, and BF) and tests two labeling strategies. The tutoring path, which translates labeled TEM images into the target modality, trains an nnU-Net on the synthetic images with the recycled annotations, and applies it to real target images, achieves a mean Dice score of $0.736 \pm 0.005$ for axons and $0.652 \pm 0.005$ for myelin on the SEM dataset. The adaptive path, which translates unlabeled target images into TEM style and applies a pre-trained TEM model, lags on SEM with axon Dice $0.238 \pm 0.088$ but surpasses tutoring on the BF dataset with axon Dice $0.586 \pm 0.025$ versus $0.454 \pm 0.010$. The paper concludes that when the domain shift is large enough to defeat a pre-trained model, translation-based pseudo labeling provides useful initial masks for manual correction.

Load-bearing premise

The pipeline assumes the TEM-to-SEM translation preserves axon and myelin geometry and boundaries, so annotations made on original TEM images remain valid for translated images and transfer to real SEM.

Editorial extensions

If this is right

On the SEM dataset, tutoring-path pseudo labels reach $0.736 \pm 0.005$ Dice for axons and $0.652 \pm 0.005$ for myelin, high enough to serve as initialization for manual refinement.
When the labeled and unlabeled domains are close (TEM versus TEM-MACAQUE), pseudo labeling adds nothing over applying the pre-trained model directly, so translation-based pseudo labeling is only worth the compute when the domain shift is large.
On the larger BF domain shift, the adaptive path outperforms tutoring, with axon Dice $0.586 \pm 0.025$ versus $0.454 \pm 0.010$; the best strategy therefore depends on translation quality in each direction.
The recommended workflow is to run the adaptive path first because it needs no extra segmentation model, and to use the tutoring path to generate a complementary set of pseudo labels at the cost of training a proxy model.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

Editorial extension: if translation preserves geometry, the same pipeline should bootstrap segmentation for other unpaired microscopy pairs (bright-field to CARS, different stains), since nothing in the method depends on electron microscopy specifically.
Editorial extension: selecting translation checkpoints by downstream segmentation Dice on a tiny manually labeled subset could outperform the cycle-reconstruction metrics (SSIM, PSNR, L1) used in the paper, and would directly test whether reconstruction quality is the right proxy for label utility.
Editorial extension: the claimed 25–50 percent annotation-time saving at Dice above 0.5 is plausible but unmeasured; a timing study of manual correction of pseudo labels versus manual annotation from scratch would give the practical benefit real numbers.

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Editorial analysis

A structured set of objections, weighed in public.

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

Desk Editor's Note

read the letter

Bottom line: this is a clean empirical paper that extends translation-based pseudo labeling from detection to segmentation, with code and real ground-truth evaluation. The headline number (0.736 Dice on SEM via the tutoring path) looks real, but the paper doesn't show it's robust to the single checkpoint choice, and the conclusion recommends a strategy that their own table contradicts.

What's genuinely useful: the tutoring-vs-adaptation comparison across three target domains is a simple, well-described experiment that gives practitioners a practical rule of thumb. The evaluation uses real labels, 5-fold CV with standard deviations, and the code is public. The TEM-MACAQUE result nicely shows when the pipeline is unnecessary, and the BF result shows a case where adaptation beats tutoring. That is a useful map of behavior.

The soft spot: checkpoint selection. Section 3 says they picked SynDiff epochs by SSIM/PSNR/L1 on the L->U->L cycle plus visual inspection, not by downstream segmentation quality. So the 0.736 ± 0.005 could be an artifact of picking a lucky epoch. The stress-test note has it right: the paper never tests whether neighboring checkpoints give similar Dice. This is addressable: report Dice across epochs or at least show that the selected checkpoint is on a plateau. Without that, the title-level claim is conditional.

Second, the baseline is only a pre-trained TEM model that returns empty masks on SEM/BF. No comparison to standard pseudo-labeling (self-training, confidence filtering) or to a CycleGAN-based translation. That makes it hard to know how much SynDiff matters.

Third, the conclusion recommends "first use the adaptive path," but on SEM the tutoring path is three times better in Dice (0.736 vs 0.238). That is not a minor wording issue; it will mislead readers. They need to state that adaptive is only preferred when compute is limited or the translation direction is easier.

The annotation-time savings (25-50%) is flagged as speculative in the text, so I don't hold it against them.

Who this is for: groups doing annotation-acceleration for microscopy, especially histology; they will get a concrete workflow and baseline numbers. It deserves a serious referee, but the revision should include checkpoint robustness, at least one stronger baseline, and a fixed recommendation.

I'd send it to review with major-revision expectations.

Referee Report

3 major / 4 minor

Summary. The manuscript proposes an unsupervised pseudo-labeling pipeline for axon/myelin segmentation in histology images. It uses SynDiff, a cycle-consistent adversarial diffusion model, to translate between an annotated TEM dataset and three unannotated target datasets (TEM-MACAQUE, SEM, and BF). Two pseudo-labeling strategies are evaluated: tutoring, where labeled TEM images are translated into the target modality and an nnU-Net is trained on the resulting synthetic images with the original labels; and adaptation, where target images are translated into the TEM style and segmented with a pre-trained TEM model. Experiments against real target-domain ground truth show that tutoring achieves a mean Dice of 0.736 ± 0.005 on SEM, while the two strategies perform comparably on TEM-MACAQUE and adaptation performs better on BF. The authors conclude that the pipeline provides useful initialization masks for manual annotation.

Significance. If the reported results are robust, the paper offers a practical way to reuse annotated TEM data for SEM and BF, where large-scale annotation is costly. The evaluation is carried out against real ground truth in the target domains, uses 5-fold cross-validation with reported standard deviations, and the code is public. The comparison of two pseudo-labeling strategies across three target datasets with increasing domain shift is a useful empirical contribution. Importantly, there is no circularity: target labels are used only for evaluation, not for training the translation model or for selecting checkpoints. The main caveats are that the headline Dice depends on a single translation checkpoint selected by reconstruction metrics, and that no alternative pseudo-labeling or domain-adaptation baseline is tested; these limit the strength of the claim that this specific pipeline is responsible for the observed result.

major comments (3)

[Section 3, Fig. 3] The headline result of 0.736 ± 0.005 for the SEM tutoring path is obtained with a single SynDiff checkpoint selected by cycle-reconstruction SSIM/PSNR/L1 and visual inspection. These metrics measure low-level fidelity to the source rather than preservation of axon and myelin geometry, so the reported Dice may be specific to the chosen epoch (50) rather than a stable property of the pipeline. I request an analysis of downstream Dice across multiple translation checkpoints, or at least a comparison between the selected checkpoint and its neighbors, to establish that the checkpoint-selection rule does not inadvertently favor a particularly good segmentation outcome.
[Section 2.3, Table 2] The only baseline for the two large-domain-shift experiments is a pre-trained TEM model that produces empty masks. To support the claim that the proposed SynDiff-based pseudo-labeling pipeline is responsible for the observed Dice scores, the paper should compare against at least one alternative unsupervised translation or domain-adaptation method, such as a CycleGAN-based version of the same tutoring protocol or a standard unsupervised domain adaptation baseline. Without such a comparison, the central result is not contextualized, and the specific contribution of the proposed pipeline remains unclear.
[Conclusion vs. Table 2] The recommendation to "first use the adaptive path" is inconsistent with the SEM results, where the adaptive path yields a mean Dice of 0.238 ± 0.088 for axons while the tutoring path yields 0.736 ± 0.005. The recommendation should be made conditional on the target domain, or the paper should provide a concrete criterion (for example, a small labeled validation set) for deciding between the two paths.

minor comments (4)

[Section 2.3] The mechanism by which the nnU-Net softmax outputs are converted to final pseudo-label masks (threshold value, post-processing, connected components) is not specified; providing this detail would improve reproducibility.
[Section 2.2/2.3] It is not stated whether the SynDiff translation model is trained on the same XU images that are later used for evaluation; if so, the evaluation is transductive. This is acceptable for a pseudo-labeling pipeline, but it should be disclosed explicitly because it affects claims about applicability to new unlabeled datasets.
[Conclusion] The claim that a Dice score above 0.5 reduces annotation time by 25-50% is unsupported by any data or citation in the manuscript; since the authors themselves call for future quantification, this sentence should be moved to future work or supported with evidence.
[Table 2] The standard deviation for the SEM adaptation path is very high (0.088 for axon Dice), and the low sensitivity (0.144) is mentioned in the text, but a brief explanation of the failure mode (for example, near-empty predicted masks) would help the reader interpret this result.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: reported Dice is an empirical evaluation against real target-domain ground truth, with no target labels used in training or checkpoint selection.

full rationale

The central claim is an experimental Dice value obtained by (i) training SynDiff on unpaired L and U images, (ii) selecting checkpoints via cycle-reconstruction metrics (SSIM/PSNR/L1) and visual inspection, and (iii) training nnU-Net on synthetic images {XL→U, YL} before evaluating on real U ground truth. None of these stages uses the target-domain annotations YU for fitting or selection; the target labels are used only in the final evaluation. The translation model is trained without target labels via unpaired cycle consistency, and the segmentation model uses only source labels YL. The checkpoint selection criterion is reconstruction quality, not downstream Dice, so the reported 0.736 Dice is not forced by construction. The only caveats—transductive use of XU and the assumption that reconstruction quality correlates with semantic preservation—are robustness and generalization concerns, not circularity. No load-bearing self-citation or uniqueness import is present; SynDiff and nnU-Net are external methods, and the AxonDeepSeg citation is background only.

Assumptions & free parameters 1 free parameters · 3 assumptions · 0 invented entities

No new entities are introduced. The central load-bearing assumptions are the semantic preservation of translation and the existence of ground truth for evaluation. One free parameter (checkpoint epoch) is selected by validation on reconstruction quality.

free parameters (1)

SynDiff checkpoint epoch selection = TEM-MACAQUE: 100, SEM: 50, BF: 40
Checkpoints chosen by reconstruction metrics (SSIM, PSNR, L1) on the L->U->L cycle, not by segmentation performance. This selection could affect downstream Dice.

assumptions (3)

domain assumption Image translation preserves the geometry of axons and myelin so that labels transfer across modalities.
The tutoring path applies TEM labels to translated SEM images; if translation distorts structure, labels are invalid. The paper does not verify semantic preservation directly, only cycle reconstruction quality.
domain assumption The target datasets are annotated for evaluation, so pseudo labels can be compared to ground truth.
The evaluation relies on ground truth labels in the unlabeled domains, contradicting the 'unlabeled' framing but enabling quantitative evaluation.
ad hoc to paper Dice score superior to 0.5 implies 25-50% annotation time reduction.
Stated in Section 4 without supporting evidence; the authors acknowledge it should be quantified in future work.

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Cite this review

Pith. "Pith review of Unpaired Modality Translation for Pseudo Labeling of Histology Images." pith.science (2026). https://pith.science/paper/BRJ5JZOX

@misc{pith2026241202858,
  author       = {Pith},
  title        = {Pith review of: Unpaired Modality Translation for Pseudo Labeling of Histology Images},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/BRJ5JZOX}},
  note         = {Machine review of arXiv:2412.02858}
}

abstract

The segmentation of histological images is critical for various biomedical applications, yet the lack of annotated data presents a significant challenge. We propose a microscopy pseudo labeling pipeline utilizing unsupervised image translation to address this issue. Our method generates pseudo labels by translating between labeled and unlabeled domains without requiring prior annotation in the target domain. We evaluate two pseudo labeling strategies across three image domains increasingly dissimilar from the labeled data, demonstrating their effectiveness. Notably, our method achieves a mean Dice score of $0.736 \pm 0.005$ on a SEM dataset using the tutoring path, which involves training a segmentation model on synthetic data created by translating the labeled dataset (TEM) to the target modality (SEM). This approach aims to accelerate the annotation process by providing high-quality pseudo labels as a starting point for manual refinement.

Figures

Figures reproduced from arXiv: 2412.02858 by the authors.

**Figure 1.** Methodology overview. (a) Initial data and model (b) Unpaired translation between L and U via Adversarial Diffusion (c) Tutorship: Training a segmentation model on synthetic data {XL→U , YL} for inference in U (d) Adaptation: Applying a segmentation model pre-trained on L to the translated images XU→L another set of pseudo labels YeU,a. We refer to this complementary process as pseudo labeling by adaptation, because… view at source ↗

**Figure 2.** Overview of the two pseudo labeling strategies using TEM as the labeled dataset and SEM as the unlabeled data. (Top) The Tutorship strategy involves generating a synthetic dataset by translating labeled TEM images and training a proxy segmentation model on this synthetic data. (Bottom) The Adaptation strategy converts unlabeled SEM images to match the TEM data distribution, enabling segmentation with a pretrained m… view at source ↗

**Figure 3.** (Top) Image translation examples. (Bottom) Unsupervised translation reconstruction losses: Structural Similarity Index Measure (SSIM), Peak Signal-to-Noise Ratio (PSNR) and L1. The error bars represent the standard deviations among the validation images [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗

Figures from the paper (1 more)

**Figure 4.** Figure 4: Pseudo labeling results. (Top) Original and translated image alongside the pre-trained model prediction and pseudo labels. (Bottom) Dice scores under 5-fold cross-validation [PITH_FULL_IMAGE:figures/full_fig_p007_4.png]

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Pith tools

Reviewed August 11, 2026 · model on record in the stance chip above.

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