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REVIEW 4 major objections 5 minor 43 references

EndoFinder: Online Lesion Retrieval for Explainable Colorectal Polyp Diagnosis Leveraging Latent Scene Representations

T0 review · 4 major / 5 minor · reviewed 2026-08-06 · deepseek-v4-flash

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Pith's one-line read Treating a polyp as a three-dimensional scene rather than a single image lets a retrieval system identify the polyp across views and infer its pathology from similar past cases.

desk verdict Solid retrieval framework with a valuable dataset, but the unstated train/test split undercuts the headline numbers. read the letter →

arxiv 2507.17323 v1 pith:CV57VQFI submitted 2025-07-23 cs.IR

Ruijie Yang , Yan Zhu , Peiyao Fu , Yizhe Zhang , Zhihua Wang , Quanlin Li , Pinghong Zhou , Xian Yang
show 1 more author
Shuo Wang
This is my paper · ORCID
classification cs.IR
keywords PolypdiagnosisScenerepresentationretrievalSemantichashingColorectalcancerColonoscopyContrastivelearningExplainableAI
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

During a colonoscopy, a doctor must often decide whether a polyp is harmless or precancerous before the pathology report exists. The paper argues that the right way to help is not a black-box classifier but a retrieval system: treat each polyp as a three-dimensional scene, fuse the several endoscopic views of it into one latent representation, then fetch the most similar historical polyps with known diagnoses. EndoFinder, built this way, outperforms single-image retrieval and fine-tuned supervised classifiers on the paper's datasets, reaching µAP 0.71 and Acc@1 0.74 on scene-based re-identification and AUC 85.59, ACC 78.75, F1 81.00 on pathology classification. The diagnosis is a majority vote of the retrieved neighbors, so the clinician can look at the supporting cases. Hashing the scene representations to binary codes makes the search fast enough for real-time use, and adding or deleting cases from the database needs no retraining.

What carries the argument

The load-bearing component is the Scene Representation Transformer: a one-layer Transformer that takes the [CLS] tokens produced by a frozen ViT-L/16 image encoder for each of four endoscopic views and fuses them, via self-attention and a learnable scene token, into one latent scene vector $S$. The scene encoder is trained with a scene-level InfoNCE loss, an entropy regularizer that pushes representations apart, and a masked-reconstruction objective in which the remaining views reconstruct a masked view through cross-attention, a pose-free mechanism. At inference, $S$ is sign-binarized into a hash code in $\{-1,1\}^K$, and a ball tree over Hamming distance retrieves the nearest historical cases. This design carries the argument because every re-identification and pathology result is a property of this fused scene space, not of any single-view feature.

What would settle it

A decisive check is to compare polyp identities across the three PolypScene datasets: if any training polyp from PolypScene-2k also appears in PolypScene-250 or PolypScene-80, the claim as stated is unsupported. The cleaner experiment is to retrain the scene encoder on a polyp-disjoint subset and evaluate on held-out polyps, or to test on an external multi-view polyp dataset; the re-identification and pathology numbers should stay close to the reported µAP 0.71 and AUC 85.59 if the result is real generalization.

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Extended reading notes

Core claim

The central claim is that polyps with similar latent scene representations share clinical characteristics, and that a retrieval-based system built on that assumption can match or beat conventional supervised classifiers while staying interpretable. The evidence is EndoFinder-S, the variant that fuses four views with a Scene Representation Transformer: on PolypScene-250 it reaches µAP 0.71 and Acc@1 0.74 for re-identifying the same polyp across views, against 0.68/0.70 for simple feature averaging, and on PolypScene-80 it reaches AUC 85.59, ACC 78.75, and F1 81.00 for pathology classification, above the best fine-tuned supervised model (ViT-L at 81.74/78.75/80.08). The same representation, binarized into hash codes and searched through a ball tree with Hamming distance, gives a 4x retrieval speedup with a 6.7% µAP drop. The authors interpret this as evidence that moving from single images to multi-view scene representations is the decisive step: the model does not learn a direct image-to-label mapping, it learns an embedding space in which historical cases with known outcomes are the explanation.

Load-bearing premise

The evaluation assumes that no polyp used to train the scene encoder on PolypScene-2k appears in the PolypScene-250 or PolypScene-80 evaluation sets; the paper never states this training/evaluation split, and if the sets overlap, the reported gains over baselines would be inflated by memorization rather than generalization.

Editorial extensions

If this is right

  • During colonoscopy, the system can show the clinician the most similar already-diagnosed polyps behind each predicted label, making the AI output checkable against real cases.
  • Diagnostic knowledge is stored in a searchable database: adding a newly confirmed polyp is an insert, and deleting a patient's case removes its retrieval influence, with no model retraining.
  • Recording several views of a polyp becomes clinically useful, since multi-view scene fusion is what lifts re-identification and classification above single-view features.
  • Hash-based search gives a 4x speedup over cosine similarity on float features with only a small drop in µAP, which is the evidence that real-time use is feasible.
  • The paper's authors propose extending the same scene-retrieval paradigm to other lesion types and to additional inputs such as patient history in future work.

Reading between the lines

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

  • Editorial inference: the cleanest test of the 'similar scene, similar label' hypothesis would be an external multi-view polyp dataset with pathology labels, since the three PolypScene datasets described in the paper come from the same data family.
  • Editorial inference: a deployment-oriented follow-up should measure how performance degrades when only one or two views can be captured during a real procedure; the paper's ablations vary view count within the same four-view dataset.
  • Editorial inference: the privacy argument for retrieval applies fully only to the database stage; deleting a row removes that case from retrieval, but the image encoder was pretrained on all of Polyp-18k, so information from those images remains in the model weights.
  • Editorial inference: the framework could be evaluated as a clinician-in-the-loop decision aid, asking whether access to retrieved reference images changes a doctor's management decision or time-to-decision, not just the model's AUC.
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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

4 major / 5 minor

Summary. The manuscript proposes EndoFinder, a retrieval-based framework for explainable colorectal polyp diagnosis. The method first trains a ViT-based image encoder using contrastive learning and masked reconstruction guided by polyp segmentation masks, then a Scene Representation Transformer that fuses multiple endoscopic views of a polyp into a latent scene representation. This representation is binarized into hash codes to enable efficient Hamming-distance retrieval from a database of historical cases, and the final diagnosis is obtained by a k-NN majority vote. The authors introduce the PolypScene-250 dataset (with the PolypScene-80 pathology-labeled subset), report re-identification and classification experiments, and claim that EndoFinder outperforms existing methods while offering interpretable diagnostic references.

Significance. The paper addresses a clinically relevant problem and offers a plausible direction for interpretable AI in colonoscopy. The release of a new multi-view polyp dataset and the source code are valuable contributions, and the idea of modeling a polyp as a 2.5D/3D scene rather than a single image is a reasonable and well-motivated extension. If the empirical results are robust, the work would show a meaningful advance for retrieval-based polyp diagnosis. However, the central 'outperforms' claims currently depend on an unstated data-split assumption and on comparisons lacking statistical justification, so the significance is not yet fully established.

major comments (4)
  1. [Section 4 / Table 1] The manuscript never states whether the 250 polyps in PolypScene-250, and its subset PolypScene-80, are disjoint from the 2,219 polyps in PolypScene-2k used to train the Scene Representation Transformer (Section 3.4, Algorithm 1). Because PolypScene-2k is described as containing 2,219 polyps and PolypScene-250 as containing 250, the evaluation sets could be subsets of the training set. If so, the re-identification results in Table 4 (µAP 0.71, Acc@1 0.74) and the classification results in Table 5 (AUC 85.59, ACC 78.75, F1 81.00) would be inflated by the model having seen the exact polyp identities during training, and the comparison to fine-tuned classifiers that were not pretrained on these scenes would be unfair. Please state explicitly whether the splits are disjoint, and if they are not, re-run the evaluation on held-out polyps or provide a clear justification for why the overlap does not affect the conclusions.
  2. [Section 5.2 / Table 5] The classification experiment on PolypScene-80 uses five-fold cross-validation on just 80 polyps, yet no error bars, confidence intervals, or significance tests are reported. With roughly 16 test samples per fold, the observed improvement of EndoFinder-S (AUC 85.59) over the best fine-tuned baseline ViT-L (AUC 81.74) may not be statistically reliable. In addition, the k-NN parameter k=6 is selected without sensitivity analysis. Please report per-fold results with standard deviations, conduct a significance test (e.g., paired bootstrap or Wilcoxon), and include an ablation over k.
  3. [Section 5.3 / Figure 5] The hash length K, which is a key hyperparameter of the binarization in Section 3.5, is never stated in the experimental section, although the speed-accuracy trade-off reported in Figure 5 depends on it. The paper also reports only a relative '4x speed improvement' without absolute query times. Please specify K and provide absolute timings (e.g., milliseconds per query) for both the float and binary retrieval cases to support the real-time claim.
  4. [Section 3.3, Eq. (4) and Section 3.4, Eq. (9)] The 'entropy' regularization term is defined as the negative logarithm of the minimum distance to any negative sample. This is not the standard entropy regularizer, the logarithm becomes negative when the minimum distance exceeds 1, and the gradient is singular as the minimum distance approaches zero. The paper provides no motivation or ablation for this choice, and the naming is misleading. Please clarify the intended behavior and either provide a theoretical or empirical justification or replace the term with a well-understood uniformity loss.
minor comments (5)
  1. [Table 3] The checkmark symbols used in Table 3 are not defined in the caption; please add an explicit statement such as '√ indicates that the corresponding view is used.'
  2. [Section 5.1.2] The sentence 'Each polyp in the PolypScence dataset has four views, which are randomly labeled as Q1, Q2, R1, and R2' should specify the random seed or the exact partition, because the re-identification results depend on which views are assigned to the query and reference groups.
  3. [Section 6] The limitations paragraph discusses dependence on segmentation masks and the need for multiple views, but it does not mention the possibility of training/evaluation overlap; once the data-split issue is resolved, this should be addressed explicitly.
  4. [Figure 5] The axis labels are not clearly readable in the figure; please add explicit axis titles and units.
  5. [References] A few references are cited in the text but appear incomplete or inconsistently formatted (e.g., the citation to 'Brearley et al., 2022' in the Introduction); please proofread the reference list.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the central claims are empirical benchmark results against external baselines, and the minor self-citation of the authors' earlier EndoFinder is contextual rather than load-bearing.

full rationale

The paper's derivation chain is self-contained and empirically benchmarked. The image encoder is pretrained on Polyp-18k, the scene encoder is trained on PolypScene-2k, and evaluation is performed on PolypScene-250 and PolypScene-80. The re-identification and pathology-classification results are measured against external baselines (DINO, SSCD, MAE, ResNet, DenseNet, ViT) under reported protocols and metrics, so the central claims do not reduce to the method's own definitions. The only self-citation is Yang et al. 2024a, the authors' earlier EndoFinder paper; it is cited as a preliminary version and as the origin of the polyp-aware image encoder, but the current framework retrains that encoder and adds a scene transformer, hashing, and new evaluation datasets. No uniqueness theorem, fitted parameter, or definitional equivalence is imported from that citation to force the reported outcomes. The unstated relationship between PolypScene-2k and PolypScene-250/80 is a data-split transparency concern that could affect validity if the sets overlap, but the paper does not assert a split or lack thereof, and circularity requires exhibiting a specific reduction from the paper's own equations or citations. No such reduction is present. Accordingly, the circularity score is 0.

Assumptions & free parameters 3 free parameters · 4 assumptions · 0 invented entities

The framework rests on standard self-supervision assumptions plus domain hypotheses: visual scene similarity tracks pathology, segmentation masks are accurate, and the scene encoder training set is disjoint from evaluation. The key unstated assumption is the dataset split, which if false inflates all headline numbers. No new physical entities are posited; the latent scene representation is a learned code and PolypScene-250 is a collected dataset artifact.

free parameters (3)
  • kNN neighborhood size k = 6
    Used for pathology classification in Section 5.2; no sensitivity analysis or selection criterion is reported, and it is chosen on the evaluation dataset.
  • Loss weights lambda, gamma, lambda_ent, lambda_rec = not reported
    Appear in Equations 5, 7, and 11; the values are absent from Section 3.6, leaving the exact objective unreproducible.
  • Hash code length K = not specified
    Section 3.5 defines codes in {-1,1}^K but K is never stated; the retrieval speed/accuracy tradeoff in Figure 5 depends on it.
assumptions (4)
  • domain assumption Polyps with similar latent scene representations likely share similar clinical labels.
    Section 3.1: the transductive hypothesis underpinning k-NN diagnosis; no direct validation that scene similarity correlates with histopathology.
  • domain assumption The polyp segmentation masks used in pretraining are accurate enough to guide masking.
    Section 3.3 and the Limitations section; the authors themselves note that segmentation errors could propagate into the encoder.
  • domain assumption The four views assigned to each polyp in the PolypScene datasets belong to the same physical lesion, and the training and evaluation sets are disjoint.
    Sections 3.4 and 4; the paper does not describe view-association verification and does not state the train/eval split, which is load-bearing for the reported numbers.
  • domain assumption Sign binarization with Hamming distance preserves enough ranking fidelity for retrieval.
    Section 3.5; only assessed empirically in Figure 5, not guaranteed by construction, and the hash length is unspecified.

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Cite this review

Pith. "Pith review of EndoFinder: Online Lesion Retrieval for Explainable Colorectal Polyp Diagnosis Leveraging Latent Scene Representations." pith.science (2026). https://pith.science/paper/CV57VQFI

@misc{pith2026250717323,
  author       = {Pith},
  title        = {Pith review of: EndoFinder: Online Lesion Retrieval for Explainable Colorectal Polyp Diagnosis Leveraging Latent Scene Representations},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/CV57VQFI}},
  note         = {Machine review of arXiv:2507.17323}
}
read the original abstract

Colorectal cancer (CRC) remains a leading cause of cancer-related mortality, underscoring the importance of timely polyp detection and diagnosis. While deep learning models have improved optical-assisted diagnostics, they often demand extensive labeled datasets and yield "black-box" outputs with limited interpretability. In this paper, we propose EndoFinder, an online polyp retrieval framework that leverages multi-view scene representations for explainable and scalable CRC diagnosis. First, we develop a Polyp-aware Image Encoder by combining contrastive learning and a reconstruction task, guided by polyp segmentation masks. This self-supervised approach captures robust features without relying on large-scale annotated data. Next, we treat each polyp as a three-dimensional "scene" and introduce a Scene Representation Transformer, which fuses multiple views of the polyp into a single latent representation. By discretizing this representation through a hashing layer, EndoFinder enables real-time retrieval from a compiled database of historical polyp cases, where diagnostic information serves as interpretable references for new queries. We evaluate EndoFinder on both public and newly collected polyp datasets for re-identification and pathology classification. Results show that EndoFinder outperforms existing methods in accuracy while providing transparent, retrieval-based insights for clinical decision-making. By contributing a novel dataset and a scalable, explainable framework, our work addresses key challenges in polyp diagnosis and offers a promising direction for more efficient AI-driven colonoscopy workflows. The source code is available at https://github.com/ku262/EndoFinder-Scene.

Figures

Figures reproduced from arXiv: 2507.17323 by the authors.

Figure 1
Figure 1. Overview of the proposed EndoFinder framework. Endoscopic multi-view images are encoded into latent scene representations and discretized [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗
Figure 2
Figure 2. Overview of the polyp-aware self-supervised representation learning framework. The model jointly learns representations by contrastive [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. Overview of the multi-view scene representation learning framework. Polyp images from multiple views are encoded individually via a frozen [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figures from the paper (3 more)
Figure 4
Figure 4. Figure 4: Qualitative comparison of re-identification performance. The Query set contains two views (Q1, Q2) of a polyp. The Ref set shows the correct [PITH_FULL_IMAGE:figures/full_fig_p008_4.png]
Figure 5
Figure 5. Figure 5: The speed improvement and accuracy change brought by using [PITH_FULL_IMAGE:figures/full_fig_p010_5.png]
Figure 6
Figure 6. Figure 6: Example of nearest neighbor classification using EndoFinder-S. The table is divided into three column groups: Query, #1, and #2. Each group [PITH_FULL_IMAGE:figures/full_fig_p011_6.png]

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    " write newline "" before.all 'output.state := FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "--" = not "--" * t #2 global.max substring 't := t #1 #1 substring "-" = "-" * t #2 global.max substring 't := while if t #1 #1 substring * t #2 gl...

Pith tools

Reviewed August 6, 2026 · model on record in the stance chip above.