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REVIEW 3 major objections 6 minor 22 references

Challenging Vision-Language Models with Surgical Data: A New Dataset and Broad Benchmarking Study

T0 review · 3 major / 6 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read Generalist vision-language models handle surgical images as well as natural images, but perform poorly when questions require medical knowledge, and specialized medical VLMs trail generalists.

desk verdict Useful new surgical VQA benchmark, but the cross-domain comparability claim rests on an undisclosed question filter that needs to be addressed. read the letter →

arxiv 2506.06232 v2 pith:ICB3FKWZ submitted 2025-06-06 cs.CV

Leon Mayer , Tim Rädsch , Dominik Michael , Lucas Luttner , Amine Yamlahi , Evangelia Christodoulou , Patrick Godau , Marcel Knopp
show 3 more authors
Annika Reinke Fiona Kolbinger Lena Maier-Hein
This is my paper
classification cs.CV
keywords SurgicalSceneUnderstandingVision-LanguageModelsEndoscopicVQABenchmarkingLaparoscopicsurgeryFoundationMedicalHeiCo-VQA-BaseCriticalViewofSafety
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

This paper asks whether current vision-language models (VLMs) actually understand laparoscopic and endoscopic surgical images. To answer it, the authors build HeiCo-VQA-Base, a surgical visual question-answering benchmark with 167,384 questions over 24,252 frames, and convert existing surgical datasets into VQA tasks of rising difficulty. Their central finding is that state-of-the-art generalist VLMs solve basic perception questions on surgical images about as accurately as they do on natural images, despite surgical images occupying a distinctive encoding space. Performance collapses, however, when questions require medical knowledge, and specialised medical VLMs consistently trail size-matched generalist models. The paper concludes that future progress should come from injecting surgical knowledge into large generalist models rather than from training small domain-specific ones.

What carries the argument

The evaluation machinery is a VQA-benchmarking pipeline that turns instance segmentations into question-answer pairs: Depth Anything V2 supplies relative depth, and a set of rules converts object attributes (size, colour, depth, spatial relations) into questions across the seven Blink-style categories (condition, spatial, colour, detection, interaction, depth, puzzle). The pipeline is applied to the Heidelberg Colorectal (HeiCo) dataset to create HeiCo-VQA-Base, and the same prompting harness is used on SSG-VQA, Endoscapes-CVS, and Kvasir-VQA for advanced tasks. Performance is measured with an Accuracy%(threshold) metric—the fraction of frames where at least a given share of questions is answered correctly—and with Matthews Correlation Coefficient to separate true skill from random guessing.

What would settle it

Run the same benchmark on a surgical-specific version of the excluded natural-domain question types—instrument orientation, mirroring, and rotation checks—and compare the Accuracy% curves across domains; if surgical scores fall clearly below natural ones on those questions, the parity conclusion would not survive.

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Extended reading notes

Core claim

The paper's central claim is threefold: (1) on basic perception tasks—counting, localisation, spatial relations, colour, detection, depth—zero-shot generalist VLMs perform at the same level on surgical images as on natural image domains; (2) on advanced frame-based scene understanding such as Critical View of Safety assessment and scene-graph reasoning, all models fall toward random chance as complexity grows; and (3) medically fine-tuned VLMs underperform size-matched generalists on every dataset and complexity level. The authors interpret these results as evidence that current VLMs' failure on surgery is not a visual perception failure but a knowledge failure, and that the field's next step should be to embed surgical knowledge into generalist architectures.

Load-bearing premise

The claim that surgery is as easy as natural images assumes that removing questions that cannot reasonably be applied to surgery did not systematically remove the hardest perceptual questions; if it did, the parity would be an artifact of question selection.

Editorial extensions

If this is right

  • Researchers should expect generalist VLMs to handle basic surgical perception zero-shot, so surgical tools can reuse generalist vision encoders.
  • The bottleneck for surgical AI is medical knowledge, not low-level image understanding; benchmarks and training should target anatomy, instrument names, and safety criteria.
  • Large generalist models should serve as baselines for future medical VLM research, not small domain-tuned models.
  • Reasoning-oriented models did not improve on complex surgical tasks, suggesting that chain-of-thought alone does not compensate for missing surgical knowledge.
  • The released benchmark provides a common yardstick for comparing future surgical VLMs against humans and random chance.

Reading between the lines

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

  • An implication the paper leaves implicit is that its cross-domain comparability depends on which natural-image questions were filtered out; if the removed perceptual checks (e.g., orientation and rotation) are precisely where models fail, the reported parity may understate surgical difficulty.
  • A testable extension would be to adapt the filtered-out question types to surgical instruments—replacing generic rotation with instrument orientation or mirroring—and re-measure the domain gap.
  • The underperformance of medical VLMs may reflect limited surgical content in their fine-tuning data rather than an architectural limit; a matched-scale fine-tuning experiment on surgical data would separate these explanations.
  • In clinical terms, the near-chance performance on Critical View of Safety assessment suggests that current VLMs cannot be trusted for intraoperative decision support without a human in the loop.
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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 / 6 minor

Summary. This manuscript presents a large-scale benchmarking study of vision-language models (VLMs) on laparoscopic and endoscopic images. The authors introduce HeiCo-VQA-Base, a new VQA benchmark built from the HeiCo colorectal dataset using a rule-based question-generation pipeline over instance segmentations and depth priors, with human reference annotations on a subset of questions. They address three research questions: whether VLMs solve basic perceptual questions on surgical images as well as on natural images (RQ1), how performance degrades on advanced frame-based scene-understanding tasks from SSG-VQA, Endoscapes-CVS, and Kvasir-VQA (RQ2), and whether medical fine-tuned VLMs outperform size-matched generalist models (RQ3). The main reported findings are that generalist VLMs perform comparably on basic surgical and natural perception tasks, that performance deteriorates as tasks require more medical knowledge and reasoning, and that medical VLMs underperform matched generalists. The paper concludes that the field should focus on injecting surgical knowledge into generalist models rather than training small domain-specific models.

Significance. The benchmark and evaluation are a useful community resource: the authors release a public dataset derived from the HeiCo data, provide human baselines on roughly 15.8k question-answer pairs, and evaluate a broad set of proprietary and open-weight models under consistent prompting and temperature settings. If the central claims hold, the paper provides decision-relevant evidence that current VLM limitations on surgical data lie more in medical knowledge injection than in basic visual perception, and that specialized medical VLMs today do not justify their small size. The framework is described as reusable and the underlying datasets are public, which strengthens reproducibility; a release of the question-generation pipeline itself would further strengthen it. The main weaknesses are that the cross-domain comparability argument rests on an undisclosed filtering step, that several quantitative conclusions lack uncertainty estimates, and that the reported dataset image count is inconsistent between the abstract and the methods.

major comments (3)
  1. [§2.3, RQ1; Fig. 3b] The central RQ1 claim that basic perception performance on surgical images is 'comparable' to natural domains depends on the stated filtering of the benchmark-generation questions from [10] 'by removing questions that cannot reasonably be applied to the surgical domain (e.g. determining whether an image is rotated).' The manuscript does not list the removed templates or categories, does not state whether the same filter was applied symmetrically to the natural-domain runs, and does not analyze the difficulty of the removed questions. If rotation/orientation-type questions are among the harder categories, or if the filter is applied only on the surgical side, the overlapping curves in Fig. 3b could reflect a change in question mix rather than in visual capability. Please disclose the full list of filtered templates, report per-category accuracy for surgery and each natural domain, and show a sensitivity analysis in which the same excluded templates are removed from the natural-domain curves; without this, the 'comparable to natural images' conclusion is not yet established.
  2. [Abstract; §1; §2.2] The dataset size is reported inconsistently: the abstract and the introduction state that HeiCo-VQA-Base contains 24,252 images, while §2.2 describes 10,040 frames from the HeiCo dataset and derives 167,384 question-answer pairs from these frames (10,040 × 16.7 ≈ 167,384). Since the dataset release is one of the paper's main contributions, the correct image count must be stated consistently and verified against the released data.
  3. [§3, RQ2/RQ3; Figs. 5–6] The advanced-task results are presented as point estimates of MCC with no confidence intervals, error bars, or statistical tests, even though the frames used for SSG-VQA, Endoscapes-CVS, and Kvasir-VQA are random samples of 500–2,000 frames. Several conclusions rest on small differences between models, such as QvQ being worse than the 7B and 72B Qwen variants, or medical models underperforming matched generalists on particular datasets. Because the evaluation is based on random sampling, the authors should report bootstrap confidence intervals or otherwise quantify sampling variability for each MCC point in Fig. 5 and Fig. 6; otherwise the model ordering cannot be distinguished from noise.
minor comments (6)
  1. [Fig. 6] The caption says 'For all advanced tasks' but the panel includes HeiCo-VQA-Base, which is described in §2.2 as a basic-perception dataset; adjust the caption or the figure.
  2. [Fig. 3b] The caption states that bands represent the standard deviation across models only for the surgery domain, although all curves are averages across models; show the corresponding bands for natural domains or explain why they are omitted.
  3. [§2.3, RQ2] The sampling of frames for SSG-VQA (500), Endoscapes-CVS (1,000), and Kvasir-VQA (2,000) is described without stratification or clustering details; state whether frames were sampled per video and whether multiple frames from the same video could affect independence.
  4. [§2.2] The statement that 'an average of 17 questions' per frame yields 167,384 question-answer pairs should be accompanied by the range and standard deviation of questions per frame, since the per-image Accuracy%(t) aggregation depends on the number of questions per image.
  5. [§3, RQ3] The medical Mini-InternVL models are not described; please specify the fine-tuning data, training protocol, and references for these medical adaptions so that RQ3 can be interpreted.
  6. [Fig. 3b] The human performance curve is not described in the methods; it is unclear whether it is computed on the same question subset as the model curves, how annotator disagreement was resolved, and whether the same Accuracy%(t) metric was used; add a methods description for the human baseline.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the benchmark construction and model evaluations are independent, and the self-citation to [10] is a reusable external framework rather than a load-bearing circular premise.

full rationale

The paper is an empirical benchmarking study, not a derivation chain. Ground-truth labels for HeiCo-VQA-Base are generated from instance segmentations (HeiCo), depth priors (Depth Anything V2), and rule-based transformations, plus human reference annotations for 15,844 question-answer pairs. None of these inputs come from the VLMs being evaluated. The random baselines are computed by uniform random answer selection, independently of model outputs. The advanced-task evaluations use external datasets (SSG-VQA, Kvasir-VQA, Endoscapes-CVS) with their own annotations. The central claims about model performance therefore do not reduce to the benchmark construction. The reuse of the authors' prior framework [10] for RQ1 is a potential overlap, but [10] is a benchmark-generation pipeline, not a fitted parameter or a self-cited uniqueness theorem; the natural-domain comparisons are produced by applying the same generator to natural and surgical domains. The undisclosed filtering of natural-domain questions (Sec. 2.3) is a validity and comparability concern, not a circularity: the filter may affect question difficulty, but it does not make the evaluated predictions equal to the benchmark's construction. No equation in the paper defines a predicted result in terms of its own input, and no load-bearing claim is justified solely by a self-citation. Accordingly, the circularity score is 0.

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

The benchmark's ground truths come from external annotations, depth priors, and rule templates rather than from the tested VLMs, so the core evaluation is not fitted. The listed assumptions concern label reliability, question filtering, sampling representativeness, and prompt equivalence; they are domain assumptions that a replication study should test.

assumptions (5)
  • domain assumption Rule-based facts generated from instance segmentations and Depth Anything V2 depth estimates define correct answers for basic perception questions.
    Sec 2.1 pipeline converts segmentations plus depth into facts; any depth or attribute errors propagate into the ground truth labels.
  • domain assumption Human reference answers collected by an external annotation service with early stopping after 3 to 6 annotations are reliable gold standards.
    Sec 2.2 states 15,844 human-annotated QA pairs; no inter-annotator agreement or adjudication is reported.
  • domain assumption Filtering out natural-domain questions that do not apply to surgery preserves a valid comparison of basic perception across domains.
    Sec 2.3 removes questions such as rotation checks; this changes the question distribution for surgery relative to natural domains.
  • domain assumption Random sampling of 500 to 2,000 frames from SSG-VQA, Kvasir-VQA, and Endoscapes yields representative difficulty for advanced tasks.
    Section 2.3 describes random sampling without stratification; no sampling variability is reported.
  • domain assumption Appending answer lists to prompts for advanced tasks converts classification tasks into equivalent multiple-choice queries for all models.
    Section 2.3 states answer lists were appended; differences in instruction-following may affect model scores.

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Cite this review

Pith. "Pith review of Challenging Vision-Language Models with Surgical Data: A New Dataset and Broad Benchmarking Study." pith.science (2026). https://pith.science/paper/ICB3FKWZ

@misc{pith2026250606232,
  author       = {Pith},
  title        = {Pith review of: Challenging Vision-Language Models with Surgical Data: A New Dataset and Broad Benchmarking Study},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/ICB3FKWZ}},
  note         = {Machine review of arXiv:2506.06232}
}
read the original abstract

While traditional computer vision models have historically struggled to generalize to endoscopic domains, the emergence of foundation models has shown promising cross-domain performance. In this work, we present the first large-scale study assessing the capabilities of Vision Language Models (VLMs) for endoscopic tasks with a specific focus on laparoscopic surgery. Using a diverse set of state-of-the-art models, multiple surgical datasets, and extensive human reference annotations, we address three key research questions: (1) Can current VLMs solve basic perception tasks on surgical images? (2) Can they handle advanced frame-based endoscopic scene understanding tasks? and (3) How do specialized medical VLMs compare to generalist models in this context? Our results reveal that VLMs can effectively perform basic surgical perception tasks, such as object counting and localization, with performance levels comparable to general domain tasks. However, their performance deteriorates significantly when the tasks require medical knowledge. Notably, we find that specialized medical VLMs currently underperform compared to generalist models across both basic and advanced surgical tasks, suggesting that they are not yet optimized for the complexity of surgical environments. These findings highlight the need for further advancements to enable VLMs to handle the unique challenges posed by surgery. Overall, our work provides important insights for the development of next-generation endoscopic AI systems and identifies key areas for improvement in medical visual language models.

Figures

Figures reproduced from arXiv: 2506.06232 by the authors.

Figure 1
Figure 1. Research questions addressed in this study and sample images illustrating the nature and complexity of basic perception and advanced tasks. literature: First, while several endoscopic VQA datasets exist, the hetero￾geneity of questions prevents meaningful comparison with natural domain performance. Second, most endoscopic VQA studies utilize small, specific models without assessing state-of-the-art (SOTA) VLMs at sc… view at source ↗
Figure 2
Figure 2. The basic perception tasks cover a broad range of question categories. Example questions rephrased for brevity. 2.1. Framework for VQA Benchmarking To investigate the RQs depicted in [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Basic perception tasks are solved with comparable accuracy on sur￾gical and natural images. (a) PCA Visualization of the Qwen2 VL 7B vision encoder showing that surgical images (red) are encoded differently from natural ones. (b) Despite this, SOTA models perform similarly when solving basic perception tasks on the different domains. Each curve represents the average performance across models according to the Accura… view at source ↗
Figures from the paper (3 more)
Figure 4
Figure 4. Figure 4: Vision-Language models struggle on similar tasks in surgery as in other domains. Performance on various task categories summarized in [PITH_FULL_IMAGE:figures/full_fig_p007_4.png]
Figure 5
Figure 5. Figure 5: (a) Matthews Correlation Coefficient (MCC) is depicted as the func￾tion of task complexity for all advanced tasks on laparoscopic data (left)and separately for endoscopic data (right). (b) Example questions rephrased for brevity. RQ3: Comparison of Specialized Medical …
Figure 6
Figure 6. Figure 6: Medically fine-tuned models fail to outperform matched generalist models on surgical questions. For all advanced tasks, Matthews Correlation Coefficient (MCC) is depicted for the medical model, a size-matched generalist and the state-of-the-art generalist Qwen2VL-72B. …

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Reviewed August 7, 2026 · model on record in the stance chip above.