CytoFM: The first cytology foundation model

Ashwath Radhachandran; Corey Arnold; Dongwoo Lee; Ekaterina Redekop; Shreeram Athreya; Vedrana Ivezi\'c; Vivek Sant; William Speier

REVIEW 3 major objections 4 minor 27 references

CytoFM: The first cytology foundation model

T0 review · 3 major / 4 minor · reviewed 2026-08-16 · deepseek-v4-flash

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Pith's one-line read CytoFM, the first cytology-specific foundation model, learns transferable cell representations that beat non-cytology foundation models on two of three downstream cytology classification tasks, including a dataset completely unseen during…

desk verdict A genuinely first cytology foundation model with a plausible transfer signal, but the same-dataset evaluation splits and missing error bars on the unseen test make the 'two of three' claim weaker than the abstract suggests. read the letter →

arxiv 2504.13402 v1 pith:36FIWZME submitted 2025-04-18 cs.CV

Vedrana Ivezić , Ashwath Radhachandran , Ekaterina Redekop , Shreeram Athreya , Dongwoo Lee , Vivek Sant , Corey Arnold , William Speier This is my paper · ORCID

classification cs.CV

keywords cytologyfoundationmodelself-supervisedpretrainingiBOTvisiontransformermultipleinstancelearningcervicalbreastcellmorphology

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

CytoFM is presented as the first foundation model pretrained specifically on cytology images rather than on natural images or tissue slides. The paper's claim is that a Vision Transformer trained without labels—by a combination of masked-image prediction and self-distillation—on roughly 1.4 million cell patches from breast, cervix, and thyroid learns transferable representations of cell morphology. On two of three downstream classification tasks, including a cervical dataset the model never saw, those representations beat a histopathology foundation model and an ImageNet-trained model; on the third task, breast malignancy classification, CytoFM trails the ImageNet model. If the claim holds, cytology would gain a reusable feature extractor for diagnostic tasks that each have too few labels to train a model from scratch.

What carries the argument

The load-bearing object is CytoFM, a ViT-Base feature extractor pretrained with iBOT. iBOT trains a teacher-student pair with two objectives: self-distillation, where the student's class token must match the teacher's predictions on a differently augmented view, and masked image modeling, where the student predicts patch tokens for masked regions from the teacher's unmasked embeddings. This pairing lets one frozen encoder represent both coarse context, such as cellularity and cell clusters, and fine detail, such as nuclear boundaries and mitotic activity. For evaluation, patch features are pooled by an attention-based multiple instance learning head, so predictions are made at the image or slide level without patch-level labels.

What would settle it

A direct test: retrain CytoFM with all MLBC and FNAC2019 images removed from the pretraining corpus and rerun the same downstream protocols. If the reported MLBC and FNAC2019 margins over UNI and iBOT-ImageNet shrink or disappear, then the apparent advantage comes from training/test distribution overlap rather than from cytology-specific representation learning; the HiCervix result alone would then be the only clean transfer evidence.

Watch

Extended reading notes

Core claim

CytoFM is a frozen ViT-Base encoder obtained by continuing iBOT pretraining on a multi-institutional, multi-organ cytology corpus. On a held-out split of the MLBC cervical dataset, it reaches 0.930 ± 0.05 accuracy and 0.993 ± 0.01 AUROC (area under the ROC curve), significantly above UNI (0.895/0.986) and iBOT-ImageNet (0.879/0.983). On the completely unseen HiCervix dataset, it reaches 0.844 accuracy and 0.968 AUROC, ahead of UNI (0.800/0.952) and iBOT-ImageNet (0.803/0.956). On FNAC2019 breast malignancy classification it scores 0.908 accuracy and 0.979 AUROC, the lowest of the three models, which the authors attribute to breast cytology being only a small fraction of the pretraining data. The paper reads these results, together with attention maps that highlight nuclei, mitotic figures, and cell boundaries, as evidence that cytology-specific self-supervised pretraining captures features that general-purpose encoders miss.

Load-bearing premise

The evaluation assumes that splitting the same dataset into disjoint pretraining and test portions measures generalization; because the pretraining and held-out FNAC2019 and MLBC images come from the same acquisition and labeling process, shared staining and scanner characteristics could inflate CytoFM's scores on those two tasks.

Editorial extensions

If this is right

CytoFM's frozen features place it ahead of both baselines on cervical cell typing (MLBC accuracy 0.930, AUROC 0.993), so practitioners using a cytology-specific backbone may not need to train separate encoders for each cervical cytology task.
On a dataset the model never saw (HiCervix), CytoFM still leads (accuracy 0.844, AUROC 0.968), suggesting the learned features transfer across institutions and scanners without stain normalization or fine-tuning.
On breast FNAC2019, where breast patches were only a small part of pretraining, CytoFM trails iBOT-ImageNet; the paper's explanation implies that balancing organ representation in pretraining should improve cross-organ performance.
The reported gains justify further investment in larger, more balanced cytology pretraining corpora and in probing which cytology-specific features, such as mitotic activity and nuclear boundaries, drive the downstream improvements.

Reading between the lines

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

A stronger test of the 'cytology-specific' claim would evaluate CytoFM on an organ absent from pretraining, such as urine or lung cytology; HiCervix is unseen but still cervical, so it mainly proves cross-institution transfer within the dominant organ.
The two partially held-out evaluations could be rerun with pretraining ablations that remove MLBC and FNAC2019 entirely, separating the effects of dataset overlap from genuine transfer.
If the HiCervix margin holds under such ablations, cytology foundation models could become the default frozen backbones for rare cytology tasks where labels are too scarce to train a supervised model from scratch.
The attention maps point to a concrete clinical extension the paper does not pursue: using CytoFM's attention as a screening signal for mitotic activity, which could be validated against expert annotations.

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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

Short version: this is the first self-supervised cytology foundation model I know of, and the authors deserve credit for assembling a multi-institutional pretraining corpus and showing that domain-specific pretraining helps in at least one clean out-of-distribution setting. But the evidence for "two of three" is not as strong as the abstract implies. The MLBC win is confounded by non-patient-level splitting, and the HiCervix win is reported without error bars.

The genuinely new thing is empirical: they train an iBOT ViT-Base on ~1.4M patches from eight cytology datasets spanning breast, cervix, and thyroid, and compare frozen features via ABMIL against iBOT-ImageNet and UNI. The HiCervix result — accuracy 0.844 vs 0.803/0.800, AUC 0.968 vs 0.956/0.952 — is the only clean generalization test, because that dataset is completely unseen. That result is a real positive, even if modest.

The soft spots are in the evaluation protocol. FNAC2019 and MLBC are split into "non-overlapping sets" for pretraining and evaluation, but MLBC has 963 images from 460 patients and the split is described at the image/patch level, not patient level. So the same patient's cells can appear in both pretraining and test, and site-specific artifacts (scanner, stain) can leak. The MLBC accuracy gain (0.930 vs 0.895/0.879) could be partly distributional. The authors do report significance tests for MLBC, which helps, but it does not remove the confound. HiCervix is the clean win, but with a single fixed split and no confidence intervals, one cannot tell if 0.844 vs 0.803 is stable. That is a minor-to-moderate issue, not fatal, because the direction is consistent with the MLBC result and with the UMAP/attention visualizations.

Two smaller things: no code or model release, which is becoming the norm for foundation models and matters for reproducibility; and the private thyroid dataset relies on a custom ThyVGG filter with a top-1500 threshold, a reasonable heuristic but another free parameter. The citation pattern is fine, and the discussion honestly flags the breast/cervix imbalance and the comparison to HiCervix's HeirSwin state of the art.

Bottom line: this is a paper I would send to a serious referee. The central claim that cytology-specific self-supervised pretraining helps is plausible and partially supported; the protocol needs patient-level splits and error bars on HiCervix. I would not desk reject it, but I would expect major revisions.

Referee Report

3 major / 4 minor

Summary. This manuscript describes CytoFM, a ViT-Base encoder pretrained with iBOT on approximately 1.4 million image patches from seven public cytology datasets and one private thyroid dataset. The pretrained encoder is frozen and its features are aggregated with an attention-based multiple instance learning (ABMIL) framework for three downstream tasks: malignancy classification on FNAC2019 and cell-type classification on MLBC and HiCervix. The authors compare CytoFM against iBOT-ImageNet and UNI and report that CytoFM outperforms both baselines on MLBC and HiCervix but underperforms on FNAC2019. They also provide UMAP visualizations of feature embeddings and attention maps as qualitative evidence that CytoFM learns cytologically relevant features.

Significance. If the empirical claims hold, CytoFM would be a useful contribution as the first cytology-specific self-supervised foundation model. The comparison against a histopathology foundation model (UNI) and a natural-image model (iBOT-ImageNet) is appropriate and informative. Strengths of the paper include the use of multiple public datasets, the inclusion of a completely unseen evaluation dataset (HiCervix), significance testing over 100 random splits for two of the three tasks, and the qualitative UMAP and attention-map analyses. However, the evaluation protocol does not yet fully support the central 'two of three' generalization claim: the two partially held-out datasets use same-dataset image-level splits rather than patient-level splits, and the clean out-of-distribution result on HiCervix is reported from a single fixed split without confidence intervals or a significance test. These issues are fixable with additional experiments and statistical reporting, but they are load-bearing for the main claim.

major comments (3)

[§3.1, Table 2] The held-out portions of FNAC2019 and MLBC are formed by splitting the same datasets used for pretraining into 'two non-overlapping sets' at the image or patch level, not at the patient level. MLBC is described as containing 963 images from 460 patients, so the current split can place images from the same patient or the same scanner/stain protocol into both the pretraining set and the test set. Self-supervised pretraining can memorize site-specific acquisition artifacts, which would inflate CytoFM's performance on MLBC (accuracy 0.930 versus 0.895 for UNI and 0.879 for iBOT-ImageNet) relative to an unseen dataset. This is the load-bearing weakness identified by the stress-test concern, and it does land: the MLBC advantage cannot be cleanly attributed to transferable cytology knowledge without a patient-level split or an equivalent control. Please report results with patient-level splits for FNAC2019 and MLBC, or otherwise demonstrate that the image-level split does not create distributional overlap.
[§3.2, Table 2] The HiCervix result is the cleanest out-of-distribution evidence for the generalization claim, yet it is reported from a single fixed train/validation/test split with no confidence intervals, error bars, or significance test. The observed accuracy gap (0.844 for CytoFM versus 0.803 for iBOT-ImageNet and 0.800 for UNI) is promising, but it cannot by itself carry the 'two of three' claim. Please add bootstrap confidence intervals or repeated-seed variability estimates and perform a statistical comparison against the baselines on the same fixed split. Without this, the HiCervix gain is not quantitatively established.
[§2.2, Figure 2; §4] The private thyroid dataset is filtered with the custom ThyVGG model using a top-1500 patch threshold, and the paper acknowledges in §4 that breast cytology contributes only 2,544 images while cervical cytology contributes close to a million. Since no downstream thyroid evaluation or ablation is presented, the claim that CytoFM learns representations generalizing across organs is not currently supported by the experiments. Either add a thyroid evaluation or soften the cross-organ generalization claim in the introduction and discussion.

minor comments (4)

[§2.1] The spelling 'Papanicolau' should be 'Papanicolaou' in the description of the FNAC2019 dataset.
[§4] The method name 'HeirSwin' appears to be a typo for 'HierSwin'; please correct it and ensure the reference matches the cited work.
[§3.2, Figure 3] The attention maps in Figure 3 are selected examples and may not be representative; a quantitative analysis, such as attention-agreement with cell-level annotations, would strengthen the claim that CytoFM attends to cytologically relevant features.
[General] The manuscript does not mention a plan to release code, model weights, or the private thyroid dataset; given the emphasis on reproducibility and downstream reuse, a statement about data and code availability would be helpful.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: CytoFM's claims are empirical comparisons with disclosed pretraining; the same-dataset held-out splits are a generalization limitation, not a definitional or fitted-input reduction.

full rationale

This paper is an empirical model-comparison study, not a derivation, and I find no step in which a claimed prediction reduces by construction to a fitted parameter, a self-definition, or a load-bearing self-citation. CytoFM is pretrained with iBOT, a self-supervised method, on unlabeled cytology patches; the downstream ABMIL classifiers are trained separately on labels. No downstream label is used to fit the feature extractor, and no fitted quantity is renamed as a prediction. The held-out FNAC2019 and MLBC portions come from the same datasets used in pretraining, which raises a legitimate distributional-leakage concern about generalization, but this is an evaluation-design weakness rather than circular reasoning: the paper states the datasets are 'split into two non-overlapping sets' (Section 3.1), the compared models iBOT-ImageNet and UNI are evaluated under the same protocol, and the HiCervix dataset is 'completely unseen during model training' (Section 3.2). ImageNet-1K initialization is disclosed in Section 2.3. The only self-referential element is 'our ThyVGG model' used for patch filtering of the private thyroid data; that is a preprocessing utility and is not load-bearing for the central representation-learning claim. There are no uniqueness theorems imported from the authors, no ansatz smuggled in via self-citation, and no renaming of a known result as a new organization. The paper even acknowledges its limitation on FNAC2019 and compares HiCervix performance to an existing state of the art. Under the stated rules, absence of a quoted reduction means the correct circularity score is 0.

Assumptions & free parameters 2 free parameters · 3 assumptions · 0 invented entities

The central empirical claim relies on the modeling choices listed above. No new theoretical entities are introduced. The free parameters are dataset curation decisions rather than derivation constants.

free parameters (2)

Top-1500 patch threshold for thyroid filtering = 1500
The number of highest-probability patches selected from each thyroid slide is chosen by the authors without ablation; this choice shapes the pretraining distribution.
Magnification estimate based on nucleus pixel size = not reported
For datasets without stated magnification, the magnification is estimated from average nucleus pixel size relative to average micrometer size; the estimate is not validated.

assumptions (3)

domain assumption Patch-based analysis at 40x magnification captures the cell-level information needed for cytology classification.
The paper assumes 40x patches are appropriate for cytology, while histopathology models use 20x; this choice is motivated but not validated.
domain assumption Self-supervised pretraining with iBOT on unlabeled cytology patches yields features that transfer to downstream tasks.
The paper relies on iBOT's self-distillation and masked image modeling to learn useful representations without labels; no ablation with other SSL methods is given.
domain assumption The held-out splits from the same datasets (FNAC2019, MLBC) are representative independent test sets.
The evaluation assumes images in the held-out split are sufficiently different from the pretraining split to reflect generalization, though they come from the same source.

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Cite this review

Pith. "Pith review of CytoFM: The first cytology foundation model." pith.science (2026). https://pith.science/paper/36FIWZME

@misc{pith2026250413402,
  author       = {Pith},
  title        = {Pith review of: CytoFM: The first cytology foundation model},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/36FIWZME}},
  note         = {Machine review of arXiv:2504.13402}
}

read the original abstract

Cytology is essential for cancer diagnostics and screening due to its minimally invasive nature. However, the development of robust deep learning models for digital cytology is challenging due to the heterogeneity in staining and preparation methods of samples, differences across organs, and the limited availability of large, diverse, annotated datasets. Developing a task-specific model for every cytology application is impractical and non-cytology-specific foundation models struggle to generalize to tasks in this domain where the emphasis is on cell morphology. To address these challenges, we introduce CytoFM, the first cytology self-supervised foundation model. Using iBOT, a self-supervised Vision Transformer (ViT) training framework incorporating masked image modeling and self-distillation, we pretrain CytoFM on a diverse collection of cytology datasets to learn robust, transferable representations. We evaluate CytoFM on multiple downstream cytology tasks, including breast cancer classification and cell type identification, using an attention-based multiple instance learning framework. Our results demonstrate that CytoFM performs better on two out of three downstream tasks than existing foundation models pretrained on histopathology (UNI) or natural images (iBOT-Imagenet). Visualizations of learned representations demonstrate our model is able to attend to cytologically relevant features. Despite a small pre-training dataset, CytoFM's promising results highlight the ability of task-agnostic pre-training approaches to learn robust and generalizable features from cytology data.

Figures

Figures reproduced from arXiv: 2504.13402 by the authors.

**Figure 1.** CytoFM: the first cytology specific foundation model. Developing this model requires the curation of an unlabeled cytology specific dataset which is used to pre-train a ViT, using the iBOT framework (a). The trained ViT, CytoFM, is then used to extract features for cytology image patches; the patch features for an image are aggregated into a bag and an ABMIL framework creates a single embedding for an image to be us… view at source ↗

**Figure 2.** Filtering of patches from a WSI for our private thyroid dataset. A WSI is patched and ThyVGG is used to predict the probability that the patch contains relevant information. The top 1500 patches with the highest probability from a slide are used in the thyroid cytology dataset. we used, this was achieved by previous preprocessing of WSIs into smaller images of cytologically relevant cells by cytologists. However, in… view at source ↗

**Figure 3.** CytoFM attentions. Selected attention maps from the last layer of the model. The model attends to relevant cytological features such as the the nuclei, mitotic activity, nuclei boundaries, and morphology. the test set using the model with the best performance on the validation set. Hyperparameters for the ABMIL model are kept the same for each downstream task and across all feature extractor model comparisons. 3.2. … view at source ↗

Figures from the paper (1 more)

**Figure 4.** Figure 4: UMAP of extracted features for the MLBC (top row) and FNAC2019 (bottom row) datasets. Less dispersion in the features is seen in our model, CytoFM. morphology and areas of mitotic activity which are important in cytology classification. Therefore, cytology-specific fo…

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Reviewed August 16, 2026 · model on record in the stance chip above.

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Balazs Harangi, Gergo Bogacsovics, Janos Toth, Ilona Ko- vacs, Erzsebet Dani, and Andras Hajdu. Pixel-wise segmen- tation of cells in digitized Pap smear images.Scientific Data, 11(1):733, 2024. 3

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