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REVIEW 4 major objections 6 minor 1 cited by

NCDD: Nearest Centroid Distance Deficit for Out-Of-Distribution Detection in Gastrointestinal Vision

T0 review · 4 major / 6 minor · reviewed 2026-08-12 · deepseek-v4-flash

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Pith's one-line read This paper claims that a post-hoc score, the nearest-centroid distance deficit, separates healthy gastrointestinal landmarks from abnormalities in endoscopy images better than existing out-of-distribution scores across several…

desk verdict A useful new distance-deficit score for GI OOD detection, but the conclusion overclaims on ResNet-18 Kvasirv2 and the alpha tuning is on synthetic corruptions; worth peer review after revision. read the letter →

arxiv 2412.01590 v1 pith:4GNF5ZQM submitted 2024-12-02 cs.CV cs.AI

Sandesh Pokhrel , Sanjay Bhandari , Sharib Ali , Tryphon Lambrou , Anh Nguyen , Yash Raj Shrestha , Angus Watson , Danail Stoyanov
show 2 more authors
Prashnna Gyawali Binod Bhattarai
This is my paper · ORCID
classification cs.CVcs.AI
keywords out-of-distributiondetectionnearestcentroidgastrointestinalendoscopyfeaturespacenear-OODmedicalimagereliabilitydeeplearningclassification
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 tries to establish that a simple, post-hoc score computed from distances to class centroids in a trained classifier's feature space can flag disease as out-of-distribution in endoscopy images, where abnormalities visually overlap with healthy landmarks. The proposed NCDD score subtracts the distance to the nearest class centroid from a weighted sum of distances to all other centroids, with the weights scaled by the logarithm of the feature vector's L1 norm. Across ResNet-18, ViT, DeiT, and MLP-Mixer backbones on the Kvasir2 and GastroVision benchmarks, the paper reports AUC and FPR95 improvements over standard OOD scores such as MSP, ODIN, Energy, KNN, Neco, and FDBD. The practical stake is that a classifier trained only on normal anatomical landmarks could still alert clinicians to unseen or emerging gastrointestinal abnormalities without retraining or extra annotations.

What carries the argument

The central object is the nearest-centroid distance deficit. For a test feature vector $z$ from the penultimate layer and class centroids $\mu_c = \frac{1}{N_c}\sum_i z_i^c$, the method computes Euclidean distances $D_c = \|\mu_c - z\|_2$, takes the nearest-centroid distance $D_{\mu_n} = \min_c D_c$, and the sum of distances to every other centroid $D_{\mu_m} = \sum_{c \neq \arg\min_c D_c} D_c$. The NCDD score is $\alpha D_{\mu_m} - \beta D_{\mu_n}$, where $\alpha = \log(\|z\|_1 / 10^{\alpha_1})$ and $\beta = \log(\|z\|_1 / 10^{\alpha_2})$, with $\alpha_1$ and $\alpha_2$ tuned on synthetic validation OOD images. This machinery converts the cluster geometry induced by cross-entropy training into a one-dimensional score: in-distribution examples show a large nearest-versus-non-nearest gap, while out-of-distribution examples show a small or negative gap.

What would settle it

Run the same four backbones on Kvasir2 and GastroVision with a validation protocol that tunes alpha1 and alpha2 only on held-out real OOD disease classes rather than on synthetic noise; if NCDD's AUC and FPR95 advantage over KNN and FDBD largely disappears on the held-out class, the paper's central claim is not supported for true near-OOD disease.

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Extended reading notes

Core claim

On the paper's own terms, the discovery is a geometric regularity in the feature space of cross-entropy-trained endoscopy classifiers: in-distribution images sit closer to their ground-truth class centroid than to any other centroid, whereas out-of-distribution abnormalities, even near-OOD ones that overlap visually with healthy tissue, sit roughly equally far from all centroids. Exploiting that asymmetry, the NCDD score measures the gap between distance to the nearest centroid and the sum of distances to non-nearest centroids, then thresholds the gap to decide ID versus OOD. The paper further claims that scaling these two terms by logarithms of the feature norm makes the score more sensitive to individual OOD examples, and that this yields higher OOD detection accuracy on Kvasir2 and GastroVision than existing logit-based and feature-based scores across four architectures.

Load-bearing premise

The method's load-bearing premise is that the synthetic validation images made by corrupting normal images with random rectangles and speckle noise resemble real gastrointestinal abnormalities closely enough to tune the two score weights; if real diseases occupy a different part of feature space, the tuned weights could be miscalibrated and the reported gains could shrink.

Editorial extensions

If this is right

  • A model trained only on normal anatomical landmarks can be repurposed for abnormality flagging without retraining, simply by comparing new images with stored class centroids.
  • The score works across convolutional, transformer, and MLP-Mixer backbones, suggesting the geometric signal is not tied to one architecture.
  • Near-OOD cases such as esophagitis overlapping with the Z-line class are caught better than by nearest-neighbor-only feature-space scores.
  • Low FPR95 means fewer healthy cases are wrongly escalated to clinicians, which supports human-in-the-loop endoscopy workflows.

Reading between the lines

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

  • A natural extension the paper does not run is per-disease generalization: train on all but one abnormality and test NCDD on the held-out disease, which would directly measure whether the score generalizes to genuinely unseen pathologies rather than the fixed OOD classes in the benchmarks.
  • Because the score needs only penultimate-layer features and class centroids, it should transfer to other medical imaging domains with a small number of well-clustered in-distribution classes; this is an inference beyond the paper's gastrointestinal experiments.
  • The log-norm weighting draws on an existing observation that feature norms behave differently for OOD inputs; an untested consequence is that NCDD may be sensitive to input perturbations or preprocessing changes that alter feature magnitudes, which would be worth checking before clinical deployment.
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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 / 6 minor

Summary. The paper formulates gastrointestinal abnormality detection as an out-of-distribution (OOD) problem, treating healthy anatomical landmarks as in-distribution (ID) and pathologies (esophagitis, polyps, ulcerative colitis, etc.) as OOD. It proposes a post-hoc scoring method, Nearest Centroid Distance Deficit (NCDD), which combines the distance from a test feature vector to its nearest class centroid with the sum of distances to all other centroids, weighted by functions of the feature vector's L1 norm. The method is evaluated on Kvasirv2 and Gastrovision across four backbones (ResNet-18, ViT, DeiT, MLP-Mixer) and compared with MSP, ODIN, Energy, Entropy, MaxLogit, KNN, BLOOD, Neco, and FDBD. The main claim is that NCDD outperforms existing methods on AUC and FPR95 across these settings.

Significance. If the reported results are robust, the paper makes a useful contribution to medical OOD detection by (i) reframing GI abnormality detection as a post-hoc OOD task, (ii) introducing a simple, architecture-agnostic scoring heuristic that captures near-OOD structure, and (iii) releasing the code for reproducibility. The hypothesis that OOD examples are more equidistant from all class centroids, while ID examples are closer to the nearest centroid, is plausible and relevant for medical domains with overlapping features. However, the central claim of uniform superiority is not fully supported by the results, and the only adaptive component of the score is tuned on a synthetic OOD proxy rather than on real disease classes. These issues do not invalidate the approach but require substantial revision in claims and validation.

major comments (4)
  1. [Abstract and Conclusion (Section VI)] The abstract and the Conclusion state that NCDD outperforms prior methods on AUC and FPR95 over four model architectures, but Table II for ResNet-18 on Kvasirv2 shows NCDD's AUC (85.68) is lower than MSP (87.57), Entropy (87.55), and KNN (86.59). The claim of consistent superiority is therefore not accurate and should be qualified per architecture and dataset, or replaced with a summary statistic such as mean rank that supports a more moderate statement.
  2. [Section IV.A.2] The hyperparameters α1 and α2 in Eq. (7) are tuned on synthetic validation OOD data created by replacing a random rectangle with random values and adding speckle noise following Hendrycks et al. [40]. This proxy consists of corrupted ID imagery, not the real GI abnormality classes (esophagitis, polyps, ulcerative colitis) used as test OOD. The final α1/α2 values are not reported for any model or dataset, and Table VII only shows FPR on synthetic validation OOD for ResNet-18 on Kvasirv2. The authors should report the tuned values and assess the sensitivity of the test results to these choices, or justify that the synthetic proxy is representative of real OOD.
  3. [Section V.C, Table VI] Table VI shows that the full NCDD score reduces FPR95 from 33.60 (using Dµm alone) to 30.86, a modest absolute improvement. The paper does not report standard deviations or multiple seeds, so it is unclear whether this gain is statistically significant. Since the central claim relies on this improvement, the authors should provide variance estimates or a paired comparison across multiple runs.
  4. [Section III.B] The NCDD score in Eq. (6) is introduced as a heuristic without a formal derivation, and the underlying claim that OOD samples have similar nearest and non-nearest centroid distances is supported only by a single ablation for ResNet-18 on Kvasirv2 (Table VI). The paper would be strengthened by verifying this property across all four backbones and both datasets, and by analyzing the score's behavior when the classifier's feature space is not well clustered.
minor comments (6)
  1. [Abstract] The abstract uses 'NCCD' once ('we propose a novel nearest-centroid distance deficit (NCCD)') while the rest of the paper and the method name use 'NCDD'; please unify.
  2. [Section V.A] The heading 'Quantative Results' contains a typo; it should be 'Quantitative Results'.
  3. [Equation (5)] The notation Dµm is defined as the sum of distances to all non-nearest centroids, but the text says 'sum of its distances to all other centroids'; please clarify that it is the sum, not the mean, and make the index set explicit.
  4. [Section II] The references to Hendrycks et al. appear as 'Hendricks et al.' in two places; please correct to 'Hendrycks et al.'.
  5. [Table VI] The row for '−Dµn' shows negative values for the mean score; the sign convention and the interpretation of negative scores should be stated explicitly.
  6. [Figure 4] The t-SNE plot is difficult to parse in grayscale; consider using distinct markers and a colorblind-friendly palette to distinguish classes.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: NCDD is an empirically evaluated scoring heuristic; tuned hyperparameters are selected on synthetic validation OOD, not on the target test OOD, and no load-bearing step reduces to its own input.

full rationale

The paper proposes a post-hoc OOD score NCDD = α·Dµm − β·Dµn (Eq. 6), with α and β defined via log ∥z∥1/10^{α1} and log ∥z∥1/10^{α2} (Eq. 7). The derivation chain is not circular: the class centroids are computed from training ID features (Eq. 3), and the test-time score uses distances to those centroids. The underlying hypothesis—ID samples are closer to their ground-truth centroid while OOD samples are roughly equidistant from all class centroids—is an empirical claim, not an identity forced by the definitions. The hyperparameters α1 and α2 are tuned on synthetic validation OOD data (random rectangles plus speckle noise following Hendrycks et al.), not on the real OOD test labels; therefore the reported AUC/FPR95 values on Kvasir2 and Gastrovision are not fitted values renamed as predictions. Any concern that the synthetic corruption proxy may not match real GI abnormalities is a distribution-matching or validation risk, not a circularity in the paper's own equations. The citation to Zhang and Xiang [37] for the log-L1-norm term is an independent external result, and no self-citation chain is load-bearing. The central claim remains an empirical evaluation against external benchmarks, so no circular step is present.

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

The proposed score rests on a geometric hypothesis about classifier feature spaces (ID clusters near centroids, OOD equidistant), on Euclidean distance as a valid similarity measure, and on synthetic corrupted images as a proxy for real GI disease during hyperparameter tuning. No new physical entities are introduced; the only tuned quantities are α1, α2, and the threshold λ.

free parameters (3)
  • α1 (weight scaling hyperparameter) = not reported
    Used in α = log(||z||1 / 10^α1); tuned on synthetic validation OOD data (Section IV.A.2); final value per model and dataset is not given.
  • α2 (weight scaling hyperparameter) = not reported
    Used in β = log(||z||1 / 10^α2); tuned jointly with α1; final values are absent from the paper.
  • decision threshold λ = not reported
    Set to achieve a 95% true positive rate on ID data (Section III.A.3); standard operating point, but the exact value and cross-validation procedure are not described.
assumptions (4)
  • domain assumption Cross-entropy training makes ID examples cluster close to their class centroid and separates class centroids.
    Basis of the method, invoked in Section III.A.3 with reference [36].
  • ad hoc to paper OOD examples, being unseen, are scattered and approximately equidistant from all class centroids.
    Core hypothesis stated in Section III.B; not proven and may fail for near-OOD disease classes such as esophagitis.
  • ad hoc to paper Synthetic rectangle-plus-speckle corruption is a representative proxy for real GI abnormalities for hyperparameter selection.
    Used to tune α1 and α2 in Section IV.A.2; no evaluation is provided that synthetic OOD matches real disease appearance.
  • domain assumption Feature vectors from the penultimate layer are suitable for Euclidean centroid distance scoring.
    Assumed throughout the method; no normalization or calibration of feature spaces is performed.

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Cite this review

Pith. "Pith review of NCDD: Nearest Centroid Distance Deficit for Out-Of-Distribution Detection in Gastrointestinal Vision." pith.science (2026). https://pith.science/paper/4GNF5ZQM

@misc{pith2026241201590,
  author       = {Pith},
  title        = {Pith review of: NCDD: Nearest Centroid Distance Deficit for Out-Of-Distribution Detection in Gastrointestinal Vision},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/4GNF5ZQM}},
  note         = {Machine review of arXiv:2412.01590}
}
read the original abstract

The integration of deep learning tools in gastrointestinal vision holds the potential for significant advancements in diagnosis, treatment, and overall patient care. A major challenge, however, is these tools' tendency to make overconfident predictions, even when encountering unseen or newly emerging disease patterns, undermining their reliability. We address this critical issue of reliability by framing it as an out-of-distribution (OOD) detection problem, where previously unseen and emerging diseases are identified as OOD examples. However, gastrointestinal images pose a unique challenge due to the overlapping feature representations between in- Distribution (ID) and OOD examples. Existing approaches often overlook this characteristic, as they are primarily developed for natural image datasets, where feature distinctions are more apparent. Despite the overlap, we hypothesize that the features of an in-distribution example will cluster closer to the centroids of their ground truth class, resulting in a shorter distance to the nearest centroid. In contrast, OOD examples maintain an equal distance from all class centroids. Based on this observation, we propose a novel nearest-centroid distance deficit (NCCD) score in the feature space for gastrointestinal OOD detection. Evaluations across multiple deep learning architectures and two publicly available benchmarks, Kvasir2 and Gastrovision, demonstrate the effectiveness of our approach compared to several state-of-the-art methods. The code and implementation details are publicly available at: https://github.com/bhattarailab/NCDD

Figures

Figures reproduced from arXiv: 2412.01590 by the authors.

Figure 1
Figure 1. Landscape of clinical procedures in gastrointestinal vision. Orange: [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. The Kvasirv2 dataset [18] is formulated for OOD detection of [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗
Figure 3
Figure 3. a) Standard classification pipeline in landmark classification for Kvasirv2 [18] in-distribution dataset. In this pipeline, images fed as input will always be one of the three classes, regardless of how unrelated they are to the model’s capability. In essence, the model doesn’t know whether it is capable of making inferences on an image or not. b) Overview of our proposed OOD detection method: based on the feature r… view at source ↗
Figures from the paper (4 more)
Figure 4
Figure 4. Figure 4: Thus, we argue that considering only the distance to [PITH_FULL_IMAGE:figures/full_fig_p004_4.png]
Figure 4
Figure 4. Figure 4: t-SNE plot of Feature Space representation of the ViT model for [PITH_FULL_IMAGE:figures/full_fig_p005_4.png]
Figure 5
Figure 5. Figure 5: Qualitative comparison of our method and other SOTA OOD methods for Kvasirv2 on ViT model: OOD examples over-confidently predicted by the [PITH_FULL_IMAGE:figures/full_fig_p008_5.png]
Figure 6
Figure 6. Figure 6: Performance of our method on FPR95↓ on separate te OOD classes for Kvasirv2 on ViT model. It is evident that, out of four OOD examples, NCDD outperforms KNN-OOD by a significant margin. TABLE VII HYPERPARAMETER STUDY FOR NCDD ON RESNET-18 WITH SYNTHETIC VALIDATION OOD …

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Forward citations

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Pith tools

Reviewed August 12, 2026 · model on record in the stance chip above.