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REVIEW 3 major objections 4 minor 40 references

Leveraging Computational Pathology AI for Noninvasive Optical Imaging Analysis Without Retraining

T0 review · 3 major / 4 minor · reviewed 2026-08-12 · deepseek-v4-flash

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Pith's one-line read Converting OCT and RCM scans into H&E-like images lets off-the-shelf pathology AI analyze them accurately without retraining.

desk verdict The OCT half is a solid, citable result; the RCM cell-segmentation claim is weakened by a circular ground truth and needs an independent re-annotation before the paper's strongest claims stand. read the letter →

arxiv 2411.11613 v2 pith:2DZLKFGZ submitted 2024-11-18 cs.CV

Danny Barash , Emilie Manning , Aidan Van Vleck , Omri Hirsch , Kyi Lei Aye , Jingxi Li , Philip O. Scumpia , Aydogan Ozcan
show 5 more authors
Sumaira Aasi Kerri E. Rieger Kavita Y. Sarin Oren Freifeld Yonatan Winetraub
This is my paper · ORCID
classification cs.CV
keywords computationalpathologydomaintransfervirtualstainingopticalcoherencetomographyreflectanceconfocalmicroscopyzero-shotsegmentationfoundationmodelsnoninvasiveimaging
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

The paper proposes FoundationShift, a preprocessing step that converts noninvasive optical images (OCT and RCM) into H&E-like "virtual histology" images before feeding them to off-the-shelf computational pathology models. The authors claim that this domain transfer, performed without any retraining or fine-tuning, substantially improves accuracy across tissue segmentation, cell segmentation, image retrieval, and image description. If true, it would let the large existing ecosystem of pathology AI models be applied to OCT and RCM data, bypassing the need for modality-specific annotated datasets exceeding 100,000 images. The paper reports that virtual H&E images lie much closer to the H&E training distribution, as measured by KL divergence, which the authors credit for the accuracy boost.

What carries the argument

The central object is a domain transfer network (also called virtual staining), such as OCT2Hist for OCT and the Li et al. model for RCM, which maps an optical image into an H&E-like image. These networks are trained on co-registered optical and H&E image pairs, typically using a few thousand images and micrometer-level registration accuracy. The H&E-like image is then passed to an off-the-shelf computational pathology model (SAM, MedSAM, SAM-Med2D, Hover-Net, PLIP, UNI, or GPT-4o) in zero-shot mode. The Kullback-Leibler (KL) divergence between virtual H&E and real H&E embeddings, computed with VGG16, is the explanatory metric: reducing the distance to the training domain is what the paper credits for the accuracy gains.

What would settle it

Obtain an independently annotated RCM cell segmentation ground truth, for example manual tracing by a dermatopathologist on raw RCM or a much larger CellProfiler-derived set with manual correction, then re-run the comparison on the same images or a larger cohort. If domain transfer plus Hover-Net no longer outperforms CellProfiler, or the gap narrows substantially, the central RCM cell segmentation claim fails. For the broader claim, apply FoundationShift to a noninvasive modality whose virtual staining model is known to be less accurate and check whether accuracy gains disappear as KL divergence to H&E increases.

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Extended reading notes

Core claim

FoundationShift's central claim is that converting optical images (OCT and RCM) to H&E-like images before using off-the-shelf computational pathology models significantly improves model accuracy. The paper demonstrates this across multiple models: SAM, MedSAM, and SAM-Med2D for epidermis segmentation in OCT (Dice scores rise from 0.68, 0.67, and 0.38 to 0.73, 0.80, and 0.74, respectively); Hover-Net for RCM cell segmentation, where raw Hover-Net segments zero cells but domain-transferred Hover-Net segments 4,503 cells and outperforms CellProfiler; PLIP for OCT image retrieval (recall@5 rises from 19% to 93%); GPT-4o for tissue identification (from 0% correct to over 80%); and UNI for BCC nodule classification in RCM (F1 score rises from 0.35 to 0.85). The authors also show that virtual H&E images reduce KL divergence to the H&E domain more than a comparable CT-to-MR domain transfer does.

Load-bearing premise

The RCM cell segmentation ground truth was created by running Hover-Net on the virtual H&E images and manually adjusting the results; if that ground truth is biased toward Hover-Net, the reported superiority over CellProfiler would not be supported.

Editorial extensions

If this is right

  • Domain transfer plus MedSAM yields a statistically significant Dice improvement on OCT epidermis segmentation ($p < 2 \times 10^{-15}$), with the largest gains for models that were trained on pathology images.
  • FoundationShift produces the first zero-shot cell segmentation pipeline for RCM images, since Hover-Net alone segments no cells on raw RCM but works on virtual H&E.
  • PLIP, a visual-language model with no OCT training, reaches 93% recall@5 on OCT after domain transfer, comparable to the 82% achieved on real H&E images.
  • GPT-4o identifies the tissue region in over 80% of virtual H&E images versus 0% on raw OCT, making LLM-based description of noninvasive scans feasible.
  • Because OCT and RCM are 3D modalities, FoundationShift extends 2D pathology foundation models to 3D volumes, demonstrated by a 3D epidermis and BCC nodule segmentation.

Reading between the lines

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

  • If FoundationShift generalizes to other virtual staining models (photoacoustic, Raman, two-photon), a single pathology foundation model could serve many noninvasive modalities without retraining, potentially changing deployment of optical imaging AI in dermatology and beyond.
  • The reported KL-divergence reduction predicts that models whose training domain is closest to H&E will benefit most; a testable extension is to vary domain-transfer fidelity and measure accuracy versus KL distance across modalities.
  • The RCM cell segmentation ground truth was seeded by Hover-Net on virtual H&E, so the reported margin over CellProfiler may partly reflect this construction; an independent manual ground truth or a substantially larger CellProfiler-based control would settle the unbiasedness question.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

3 major / 4 minor

Summary. The paper proposes FoundationShift, a method for applying off-the-shelf computational pathology (CPath) models to noninvasive optical images by first translating OCT and RCM images into H&E-like 'virtual histology' images using existing domain-transfer models, without retraining or fine-tuning the CPath models. The authors evaluate FoundationShift on several tasks: epidermis segmentation in OCT using SAM, MedSAM, and SAM-Med2D; cell segmentation in RCM using Hover-Net compared with CellProfiler; image retrieval using PLIP; BCC nodule classification using UNI; and image captioning using GPT-4o. The central claim is that domain transfer to H&E-like images significantly improves the accuracy of these models on noninvasive optical images, thereby enabling zero-shot use of CPath foundation models on OCT and RCM data.

Significance. The OCT segmentation results are the strongest part of the paper: they use 95 paired OCT/H&E images, expert ground truth with inter-observer agreement analysis, paired Wilcoxon tests, and consistent improvements across three segmentation models, with data and code links provided. If the RCM cell-segmentation result were independently validated, FoundationShift would be a practical and potentially impactful way to repurpose the large ecosystem of H&E-trained CPath models for noninvasive optical imaging, which is a clinically relevant direction. The paper is also commendable for evaluating multiple model families (generic segmentation, medical segmentation, cell segmentation, vision-language models, large language models) and for explicitly discussing limitations of the domain-transfer step. However, the RCM cell-segmentation evaluation is entangled with its own ground truth, and several secondary claims (UNI classification, ChatGPT analysis) rest on small or subjective evaluations. The overall significance is high if the RCM circularity can be resolved, but current evidence is mixed.

major comments (3)
  1. [Methods, RCM Cell Segmentation and Statistical Analysis; Figure 2c] The RCM cell-segmentation ground truth was created by first running Hover-Net on the virtually stained H&E images and then manually adjusting the results, as stated in this section. Because the evaluated method is exactly Hover-Net applied to the same virtual H&E input, this creates a circular evaluation: the model's own outputs seed the reference standard. The authors acknowledge that this 'may skew results in favor of Hover-Net,' but the 50-cell control using a CellProfiler-derived ground truth is insufficient: 50 cells is roughly 1% of the 4,503-cell evaluation set, the control still involves manual adjustment within the same workflow, and the reported Dice score of 0.9 between the two ground truths does not directly bound the DQ/SQ/PQ differences used for the headline comparison. This circularity is load-bearing because RCM is one of only two modalities supporting the paper's main claim, and the RCM experiment is the only quantitative cell-level result. The authors should provide an independently annotated ground truth, for example manual expert annotation on raw RCM images or on virtual H&E with an annotation protocol that does not involve Hover-Net, and re-run the comparison before claiming that FoundationShift 'significantly enhances the accuracy of ... Hover-Net' for RCM cell segmentation.
  2. [Methods, OCT Tissue Segmentation and Statistical Analysis] The bounding box prompt used for all OCT segmentation models is derived from expert annotations, with a height factor adjustment of 1.15, as described in this section. Since the same expert annotations define the ground truth used for Dice evaluation, the segmentation models receive prompt information that is already aligned with the reference standard. This is a form of ground-truth leakage that inflates absolute Dice scores and makes the reported accuracies unrepresentative of a true zero-shot deployment, where users would need to supply or generate prompts without access to expert ground truth. The relative improvement from domain transfer is likely still informative because the same prompts are used in both conditions, but the paper's claim that FoundationShift 'significantly improves model accuracy' for off-the-shelf models needs to be qualified by this protocol. Please state clearly that the evaluation assumes expert-provided bounding boxes, and, if feasible, include a comparison with automatically generated prompts (e.g., from a simple detector or a fixed grid) to assess performance in a more realistic setting.
  3. [FoundationShift Applied to Visual Language Foundation Model Classification of RCM (Supplementary Figure S7)] The UNI classification experiment is conducted on a single RCM mosaic, and the reported F1 scores depend on a cosine-similarity color mapping that the authors state was manually selected to maximize visibility of BCC nodules and minimize false negatives. This hand-tuning of the visualization threshold, combined with the single-image evaluation, means that the reported F1 values (ResNet-50: 0.46, UNI: 0.35, FoundationShift+UNI: 0.85) are not a reliable quantitative demonstration that FoundationShift 'significantly enhances' UNI. The claim should be supported by a pre-specified threshold, multiple test images, and ideally a statistical test across images. Without this, the UNI result remains anecdotal and should be presented as such rather than as a quantitative comparison.
minor comments (4)
  1. [FoundationShift Applied to Large Language Model Analysis of OCT] There is a typo in 'promt' (should be 'prompt') and the model name 'GPT-o4' appears in the text, which should be 'GPT-4o' to be consistent with the rest of the paper. Please also correct 'utilzing' and other small spelling errors throughout the manuscript.
  2. [Data availability] The paper states that all data needed to evaluate the conclusions are in the paper and/or Supplementary Materials, but the RCM data and annotation spreadsheets are provided via external Google Drive links. Please make the data availability statement more explicit by describing what files are included, and consider depositing the code and processed data in a persistent repository to improve reproducibility.
  3. [Theoretical Discussion (Supplementary Figure S2)] The KL divergence is computed on VGG16 embeddings, but the manuscript does not specify which layer or pooling is used, nor whether the values are symmetric or averaged across images. Providing these details would make the analysis reproducible and would clarify the interpretation of the reported divergence values.
  4. [OCT Tissue Segmentation and Statistical Analysis] The paper reports multiple paired Wilcoxon tests across three segmentation models but does not mention any multiple-testing correction. The p-values are extremely small, so the conclusions are unlikely to change, but a brief note about this would strengthen the statistical reporting.

Circularity Check

1 steps flagged · score 6.0 of 10

RCM cell-segmentation comparison is circular: the 4,503-cell ground truth was initialized by Hover-Net on virtual H&E, the exact pipeline being scored.

  1. self definitional [Methods, 'RCM Cell Segmentation and Statistical Analysis'; Figure 2c]
    "First, we ran Hover-Net on the virtually stained dataset, then manually adjusted the results to create a ground truth consisting of 4,503 cells. We acknowledge that generating ground truth in this method may skew results in favor of Hover-Net."

    The method being scored in Figure 2c is precisely domain transfer + Hover-Net on virtual H&E images. The reference standard used to score it was produced by running that same model on that same input modality and then manually adjusting the outputs. The reported DQ/SQ/PQ superiority over CellProfiler therefore compares CellProfiler against a ground truth initialized from Hover-Net's own segmentations; any systematic Hover-Net bias (oversegmentation, boundary placement, missed nuclei) can survive manual adjustment. The 50-cell CellProfiler-based control (Dice 0.9 between the two ground truths) covers about 1% of the 4,503 cells and measures mask overlap, not the DQ/SQ/PQ quantities used for the headline comparison, so it does not break the circularity.

full rationale

The OCT tissue-segmentation claim is evaluated against expert ground truth (two independent experts, inter-expert Dice 0.85), with the OCT2Hist domain-transfer model taken from prior published work. Although that prior work shares an author, the model was not fitted to the 95 test sections, and the with/without domain-transfer comparison is a genuine empirical evaluation; this is self-citation but not circularity. The PLIP, UNI, and ChatGPT experiments also do not involve any parameter fitted to the reported metrics. The one substantive circular step is the RCM cell-segmentation ground truth: Hover-Net's own outputs on virtual H&E initialize the 4,503-cell reference standard that is then used to show Hover-Net outperforms CellProfiler. The paper explicitly acknowledges this ('may skew results in favor of Hover-Net') and offers a 50-cell control, but the control is too small and does not directly bound the DQ/SQ/PQ differences used in the main comparison. Because RCM is one of two modalities supporting the central claim, this makes the RCM cell-segmentation result partially circular while leaving the OCT and language-model results with independent content. Overall score 6: partial circularity, not total.

Assumptions & free parameters 3 free parameters · 5 assumptions · 0 invented entities

The central claim rests on the fidelity of prior domain transfer models (OCT2Hist from ref 14, RCM virtual staining from ref 15), on the assumption that VGG16-embedding KL divergence predicts downstream model accuracy, and, for RCM cell segmentation, on the assumption that a Hover-Net-initialized ground truth is unbiased. The OCT segmentation comparison is the most independent part of the paper.

free parameters (3)
  • Bounding box height factor = 1.15
    Hand-chosen factor expanding the expert-derived bounding box prompt; applied to all segmentation models, so it does not bias the comparison, but it is a free parameter of the evaluation.
  • UNI cosine similarity threshold / color mapping = not reported
    The mapping from cosine similarity to red/green was chosen post hoc on the same image to 'maximize visibility' of BCC nodules and 'minimize false negatives'; the F1 score depends on this undisclosed threshold.
  • CellProfiler parameters = not reported
    The authors fine-tuned CellProfiler parameters to capture cells in inverted RCM images; this is a baseline configuration, not part of FoundationShift, but the exact values are not given, making the comparison hard to reproduce.
assumptions (5)
  • domain assumption KL divergence on VGG16 embeddings measures how well a domain-transferred image aligns with the CPath training domain, and this alignment predicts downstream model accuracy.
    Invoked in the Theoretical Discussion and Figure S2 to explain why FoundationShift works; no formal link between this embedding-space divergence and segmentation Dice is proven.
  • ad hoc to paper The ground truth for RCM cell segmentation, created by running Hover-Net on virtual H&E and manually adjusting outputs, is unbiased for evaluating Hover-Net.
    Stated in Methods (RCM Cell Segmentation), with an acknowledged potential skew and a small 50-cell control; this assumption is load-bearing for the RCM cell segmentation comparison.
  • domain assumption The OCT2Hist and Li et al. domain transfer models produce virtual H&E images that preserve the structures needed by downstream segmentation and classification models.
    The entire method depends on the fidelity of these prior models, which are not independently verified in this paper; only qualitative examples are shown.
  • domain assumption Expert OCT annotations with hair-follicle regions excluded are a reliable ground truth for epidermis segmentation.
    Methods report inter-expert Dice of 0.85 on 10 images; the remaining disagreement bounds the achievable Dice.
  • standard math Wilcoxon signed-rank test assumptions hold for paired Dice scores across 95 sections.
    Used for all significance statements; reasonable for paired non-normal data.

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Cite this review

Pith. "Pith review of Leveraging Computational Pathology AI for Noninvasive Optical Imaging Analysis Without Retraining." pith.science (2026). https://pith.science/paper/2DZLKFGZ

@misc{pith2026241111613,
  author       = {Pith},
  title        = {Pith review of: Leveraging Computational Pathology AI for Noninvasive Optical Imaging Analysis Without Retraining},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/2DZLKFGZ}},
  note         = {Machine review of arXiv:2411.11613}
}
read the original abstract

Noninvasive optical imaging modalities can probe patient's tissue in 3D and over time generate gigabytes of clinically relevant data per sample. There is a need for AI models to analyze this data and assist clinical workflow. The lack of expert labelers and the large dataset required (>100,000 images) for model training and tuning are the main hurdles in creating foundation models. In this paper we introduce FoundationShift, a method to apply any AI model from computational pathology without retraining. We show our method is more accurate than state of the art models (SAM, MedSAM, SAM-Med2D, CellProfiler, Hover-Net, PLIP, UNI and ChatGPT), with multiple imaging modalities (OCT and RCM). This is achieved without the need for model retraining or fine-tuning. Applying our method to noninvasive in vivo images could enable physicians to readily incorporate optical imaging modalities into their clinical practice, providing real time tissue analysis and improving patient care.

Figures

Figures reproduced from arXiv: 2411.11613 by the authors.

Figure 1
Figure 1. Tissue segmentation of OCT images utilizing FoundationShift and off-the-shelf segmentation models. a) Off-the-shelf segmentation foundation models perform poorly when segmenting OCT images. b) By performing domain transfer first, we are able to significantly improve segmentation accuracy. c,d) Segmentation examples of off-the-shelf algorithms compared to our approach (Domain Transfer + MedSAM). Ground truth expert e… view at source ↗
Figure 2
Figure 2. Cell segmentation of RCM images utilizing FoundationShift and off-the-shelf model. a) An RCM image obtained by Li et al. Cells are very difficult to visualize. Off-the-shelf cell segmentation Hover-Net is unable to segment any cells. b) By performing domain transfer first, we are able to significantly improve segmentation accuracy. We are able to reliably segment cells without any model tuning (zero shot). c) Compar… view at source ↗

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