E-BayesSAM: Efficient Bayesian Adaptation of SAM with Self-Optimizing KAN-Based Interpretation for Uncertainty-Aware Ultrasonic Segmentation

Bingsheng Huang; Bin Huang; Huiying Wen; Shuo Li; Xin Chen; Zhong Liu

REVIEW 3 major objections 3 minor 31 references

E-BayesSAM: Efficient Bayesian Adaptation of SAM with Self-Optimizing KAN-Based Interpretation for Uncertainty-Aware Ultrasonic Segmentation

T0 review · 3 major / 3 minor · reviewed 2026-08-15 · deepseek-v4-flash

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Pith's one-line read As titled, this paper claims E-BayesSAM, a token-wise variational Bayesian adaptation of SAM with a self-optimizing Kolmogorov-Arnold network, gives real-time, uncertainty-aware ultrasound segmentation at 89.0% average Dice after pruning.

desk verdict The abstract advertises an ultrasound SAM adaptation named E-BayesSAM, but the submitted full text is an unrelated mathematics paper on the Lambert W function, so every E-BayesSAM claim in this artifact is unsupported. read the letter →

arxiv 2508.17408 v1 pith:VPVGD7KT submitted 2025-08-24 cs.CV

Bin Huang , Zhong Liu , Huiying Wen , Bingsheng Huang , Xin Chen , Shuo Li This is my paper · ORCID

classification cs.CV MSC 33E2026D07

keywords E-BayesSAMultrasoundsegmentationBayesianadaptationSegmentAnythingModeluncertaintyestimationKolmogorov-ArnoldNetworktokenpruningvariationalinference

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

As titled, this paper aims to establish that the Segment Anything Model can be adapted for ultrasound segmentation in a way that is efficient, uncertainty-aware, and interpretable. The proposed E-BayesSAM reinterprets SAM's output tokens as probabilistic weights, applies training-free token-wise variational Bayesian inference to get uncertainty estimates, and uses a self-optimizing Kolmogorov-Arnold network whose learnable spline activations identify and prune redundant tokens. The reported results on five ultrasound datasets are real-time inference at 0.03 s per image, an average Dice score of 89.0% after pruning (versus 88.0% for E-BayesSAM and 88.3% for MedSAM), and the identification of four critical tokens that control SAM's decisions. The full text supplied under this header, however, is a different manuscript studying convexity of the Lambert W function, so the method and experiments behind these numbers are not present in the submitted body.

What carries the argument

The machinery is T-VBI, which reparameterizes SAM's output tokens as latent probabilistic weights so that uncertainty is obtained without training an auxiliary network, together with SO-KAN, a Kolmogorov-Arnold network with learnable spline activations that makes token contributions explicit and allows pruning. The Lambert W text uses a different mechanism: auxiliary functions $h_p(r)$ and $g_{p,q}(r)$, whose monotonicity controls the sign of $\partial F/\partial x$ in the comparison function $F(x,y)$ for $W(H_p(x,y))$ and $H_q(W(x),W(y))$.

What would settle it

Run the released code on a named public ultrasound segmentation set, compute the predictive intervals, and check empirical coverage under mild distribution shift; if coverage does not track the claimed confidence, or if the 89.0% versus 88.3% Dice ordering does not reproduce on any public split, the central claim fails. Inspect the implementation to confirm whether a variational objective is actually optimized; if none is, the 'Bayesian inference' is a deterministic transform of SAM's tokens.

Watch

Extended reading notes

Core claim

On the terms of its own abstract, the discovery E-BayesSAM proposes is that SAM's output tokens can be reparameterized as latent probabilistic weights, so that Bayesian uncertainty estimation requires no auxiliary training and no fine-tuning of the large backbone. Combined with a self-optimizing Kolmogorov-Arnold network, the token weights become interpretable enough to prune down to four critical tokens; pruning then improves the average Dice from 88.0% to 89.0%, slightly above MedSAM's 88.3%, while inference stays at 0.03 s per image. The actual full text under the same header is a mathematics paper proving necessary and sufficient conditions for strict $H_{p,q}$-convexity and concavity of the Lambert W function on $(0,+\infty)$, with parameter regions $D_1,D_2,D_3$; it does not describe E-BayesSAM, T-VBI, or any ultrasound experiment.

Load-bearing premise

The load-bearing premise is that SAM's output tokens, reparameterized as latent probabilistic weights without auxiliary training, form a genuinely calibrated Bayesian posterior; if the token-to-weight mapping is not a true posterior, the uncertainty estimates are deterministic transforms of the tokens and the Bayesian claim collapses.

Editorial extensions

If this is right

Ultrasound segmentation could run at 0.03 s per image while also returning uncertainty estimates, making SAM usable in bedside and intraoperative workflows.
Pruning to the four critical tokens would cut computation and raise accuracy from 88.0% to 89.0% average Dice, so interpretability would act as a regularizer rather than a cost.
Training-free variational inference would let SAM's large pretrained weights stay frozen during Bayesian adaptation, avoiding the instability and memory cost of fine-tuning the backbone.
The four identified critical tokens would give clinicians a concrete, auditable reason for each segmentation decision.

Reading between the lines

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

If token-level distributions are defined without optimizing any variational objective, the resulting uncertainty maps are deterministic functions of SAM's tokens; a calibration check on held-out data would separate genuine posterior uncertainty from post-hoc scaling.
The five ultrasound datasets are not named and the full text does not describe the experiments, so the 89.0% versus 88.3% Dice ordering has no reproducible basis in the provided material.
The claim that four tokens govern SAM's decisions suggests a direct test: mask all but those four tokens and measure the Dice change; if the drop is large, the 'critical token' interpretation is incomplete.
The mismatch between the E-BayesSAM abstract and the Lambert W full text means any reader trying to verify the Bayesian derivation will not find it in this submission.

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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

Quick note on arXiv:2508.17408. The abstract describes E-BayesSAM, an uncertainty-aware, interpretable adaptation of SAM for ultrasound, with real-time inference (0.03 s/image), 89.0% DSC, and token-level interpretability across five datasets. The full text is something else entirely: a mathematics paper, "Generalized Convexity of the Lambert W Function" by Gendi Wang, with its own arXiv number 2508.17409v1 [math.CA]. There is no architecture, no derivation of T-VBI, no SO-KAN description, no datasets, no experiments, no tables. The submitted artifact does not contain the paper the abstract announces.

What does the submission do well? On the claimed topic, nothing can be assessed. The Lambert W manuscript might be a reasonable piece of classical inequalities work—it has lemmas, proofs, and clearly stated parameter regions—but it is not this paper and it does not belong to the same set of authors. If this is an upload or packaging mistake, it needs to be fixed before any referee spends time. If it is deliberate, that is a much more serious integrity matter, though I would not infer intent from the abstract alone.

The consequence is straightforward: the E-BayesSAM claims rest entirely on the abstract. The central mechanism—training-free variational Bayesian inference by reinterpreting SAM output tokens as latent variables—has no supporting derivation, so the "posterior" is undefined and the uncertainty maps could be deterministic transforms of token weights. The empirical ordering (pruned 89.0 vs. E-BayesSAM 88.0 vs. MedSAM 88.3) has no error bars, no dataset names, and no split descriptions. On the evidence in front of us, the claims are unverifiable, not merely unverified. I would not call them false; I would call them unsupported.

The one thing worth keeping in mind: if the actual E-BayesSAM paper exists elsewhere with the promised code and experiments, the underlying idea—token-wise variational adaptation of SAM plus KAN-based pruning—is plausible enough to merit a look. But this submission is not that paper, and I cannot evaluate it. The right move is to desk reject or return to the authors with a request for the correct full text. Do not send this to reviewers as-is.

Referee Report

3 major / 3 minor

Summary. The submission's abstract announces E-BayesSAM, a framework that combines Token-wise Variational Bayesian Inference (T-VBI) and a Self-Optimizing Kolmogorov-Arnold Network (SO-KAN) to adapt the Segment Anything Model for uncertainty-aware ultrasound image segmentation. The abstract reports real-time inference (0.03 s/image), average Dice similarity coefficients of 89.0% (pruned E-BayesSAM), 88.0% (E-BayesSAM), and 88.3% (MedSAM) on five ultrasound datasets, and identification of four critical tokens governing SAM's decisions. The supplied full text, however, is a different paper, "Generalized Convexity of the Lambert W Function" by Gendi Wang (arXiv:2508.17409v1 [math.CA]), which has no connection to SAM, ultrasound, segmentation, T-VBI, SO-KAN, or uncertainty estimation. No architecture description, inference derivation, dataset definitions, experimental protocol, or evaluation tables for E-BayesSAM appear anywhere in the manuscript. The claims in the abstract are therefore unsupported by any verifiable content in this submission.

Significance. If fully supported, the proposed contribution would be practically valuable: an uncertainty-aware, interpretable, and real-time adaptation of SAM for medical ultrasound segmentation. The reported accuracy advantage is small (89.0% vs. 88.3% vs. 88.0% DSC), so the significance would rest on the Bayesian uncertainty estimates and the token-level interpretability rather than on raw accuracy gains. Because the manuscript contains no methods, derivations, datasets, or experiments for E-BayesSAM, the significance of the claimed results cannot be evaluated from the submitted text. The submission also provides no reproducibility artifacts beyond a GitHub URL in the abstract, and no code, configuration, or protocol is included in the manuscript.

major comments (3)

[Full text (entire submission)] The submitted full text is the paper "Generalized Convexity of the Lambert W Function" by Gendi Wang (arXiv:2508.17409v1 [math.CA]). It contains no mention of E-BayesSAM, Segment Anything Model, ultrasound, T-VBI, SO-KAN, uncertainty estimation, or segmentation, and it presents no experimental results. Consequently, every quantitative claim in the abstract—DSC values, inference time, and the identification of four critical tokens—is unsupported by the manuscript under review. This is not a local technical flaw but the absence of the object of review.
[Abstract, T-VBI description] The abstract states that T-VBI "reinterprets SAM's output tokens as dynamic probabilistic weights and reparameterizes them as latent variables without auxiliary training, enabling training-free VBI for uncertainty estimation." No derivation or definition in the manuscript specifies the variational family, the approximate posterior, the evidence lower bound, or any training objective. Without these components, the claim that the method produces Bayesian posterior uncertainty maps cannot be checked; the uncertainty maps could be deterministic transformations of token weights rather than calibrated posterior quantities.
[Abstract, experimental claims] The experimental claims are not verifiable from the manuscript: the five ultrasound datasets are unnamed, no segmentation protocol is described, no validation splits are reported, and no error bars or significance tests are given. The reported DSC differences (89.0, 88.0, 88.3 percent) are within about one percentage point, so without variance information the claimed ordering is not interpretable. Furthermore, no pruning protocol is described, leaving the claim that pruning "boosts efficiency and accuracy" open to circularity if the same data used to fit SO-KAN also select the pruning threshold.

minor comments (3)

[Abstract, wording] The abstract contains a typo ("efficienty Bayesian adaptation" should be "efficient Bayesian adaptation").
[Notation and terminology] Because the full text belongs to a different paper, none of the notations in the abstract (T-VBI, SO-KAN, token weights) are defined or contextualized in the manuscript; if the correct E-BayesSAM text is resubmitted, all abbreviations and acronyms should be expanded at first use.
[Reproducibility] The abstract mentions a GitHub repository, but the manuscript gives no commit identifier, environment specification, hyperparameters, or experiment configuration; a URL alone is insufficient for reproducibility.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity can be established because the supplied full text is an unrelated Lambert W paper, leaving no E-BayesSAM derivation chain to analyze.

full rationale

The circularity pass requires exhibiting a specific reduction in which a claimed prediction or first-principles result is equivalent, by construction or by self-citation, to its own inputs. The submitted full text is 'Generalized Convexity of the Lambert W Function' by Gendi Wang, arXiv:2508.17409v1 [math.CA], and contains no equations, architecture, or experiments pertaining to E-BayesSAM, SAM, ultrasound segmentation, or uncertainty estimation. Without the described T-VBI derivation or the SO-KAN pruning analysis, it is impossible to quote any equation that reduces a prediction to a fitted parameter or to a self-citation. The abstract's mention of 'training-free VBI' could raise a suspicion that the posterior is defined by construction from token weights, but no mathematical formulation is present to verify that suspicion, and the hard rules forbid speculation about author intent. Similarly, the absent experimental section cannot be judged as circular; it is missing support, which is a separate correctness and completeness concern, not a circularity concern. Therefore the honest finding is no demonstrated circularity, and the score is 0. The submission's failure to contain the described paper is a severe evidentiary problem, but it is not a circularity problem under the criteria provided.

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

The visible document contains only the abstract of the E-BayesSAM paper, so the ledger is reconstructed from that abstract. No free parameter values are reported. The load-bearing domain assumptions are the validity of training-free token-wise variational inference and the quality of the unnamed five datasets. No physical or theoretical entities are introduced; T-VBI and SO-KAN are architectural components, not postulated entities. This ledger should be revised when the actual methods text is available.

free parameters (2)

SO-KAN spline activation parameters
The abstract states SO-KAN uses learnable spline activations via self-supervised learning, but no values, constraints, or regularization are reported. These parameters are fitted to the training data and carry the accuracy and pruning claims.
Token pruning threshold and criterion
The abstract reports that pruning redundant tokens improves DSC from 88.0 percent to 89.0 percent, but does not state the pruning threshold or criterion; if tuned on validation data, it is a fitted quantity affecting the headline result.

assumptions (2)

domain assumption SAM output tokens can be reparameterized as dynamic probabilistic weights forming a valid variational posterior without any auxiliary training or variational optimization.
This is the core T-VBI premise stated in the abstract; if the token-to-weight mapping is not a genuine posterior, the uncertainty estimates are not Bayesian and the central reliability claim collapses.
domain assumption The five ultrasound datasets used for evaluation are correctly labeled, representative, and split without leakage.
The abstract does not name the datasets or the split; the reported DSC ordering and the four-critical-tokens finding depend on this premise.

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Cite this review

Pith. "Pith review of E-BayesSAM: Efficient Bayesian Adaptation of SAM with Self-Optimizing KAN-Based Interpretation for Uncertainty-Aware Ultrasonic Segmentation." pith.science (2026). https://pith.science/paper/VPVGD7KT

@misc{pith2026250817408,
  author       = {Pith},
  title        = {Pith review of: E-BayesSAM: Efficient Bayesian Adaptation of SAM with Self-Optimizing KAN-Based Interpretation for Uncertainty-Aware Ultrasonic Segmentation},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/VPVGD7KT}},
  note         = {Machine review of arXiv:2508.17408}
}

read the original abstract

Although the Segment Anything Model (SAM) has advanced medical image segmentation, its Bayesian adaptation for uncertainty-aware segmentation remains hindered by three key issues: (1) instability in Bayesian fine-tuning of large pre-trained SAMs; (2) high computation cost due to SAM's massive parameters; (3) SAM's black-box design limits interpretability. To overcome these, we propose E-BayesSAM, an efficient framework combining Token-wise Variational Bayesian Inference (T-VBI) for efficienty Bayesian adaptation and Self-Optimizing Kolmogorov-Arnold Network (SO-KAN) for improving interpretability. T-VBI innovatively reinterprets SAM's output tokens as dynamic probabilistic weights and reparameterizes them as latent variables without auxiliary training, enabling training-free VBI for uncertainty estimation. SO-KAN improves token prediction with learnable spline activations via self-supervised learning, providing insight to prune redundant tokens to boost efficiency and accuracy. Experiments on five ultrasound datasets demonstrated that E-BayesSAM achieves: (i) real-time inference (0.03s/image), (ii) superior segmentation accuracy (average DSC: Pruned E-BayesSAM's 89.0\% vs. E-BayesSAM's 88.0% vs. MedSAM's 88.3%), and (iii) identification of four critical tokens governing SAM's decisions. By unifying efficiency, reliability, and interpretability, E-BayesSAM bridges SAM's versatility with clinical needs, advancing deployment in safety-critical medical applications. The source code is available at https://github.com/mp31192/E-BayesSAM.

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Pith tools

Reviewed August 15, 2026 · model on record in the stance chip above.

[1] [1]

Data in brief 28, 104863 (2020)

Al-Dhabyani, W., Gomaa, M., Khaled, H., Fahmy, A.: Dataset of breast ultrasound images. Data in brief 28, 104863 (2020)

2020

[2] [2]

arXiv preprint arXiv:2308.16184 (2023)

Cheng, J., Wang, S., Liu, Y., Chen, Y., Chen, H., Li, Y.: Sam-med2d. arXiv preprint arXiv:2308.16184 (2023)

arXiv 2023

[3] [3]

In: Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 Workshops

Deng, G.D., Zou, K., Ren, K., Wang, M., Yuan, X., Ying, S., Fu, H.: Sam-u: Multi-box prompts triggered uncertainty estimation for reliable sam in medical image. In: Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 Workshops. vol. 14394 (2023)

work page 2023

[4] [4]

Ecology 26, 297--302 (1945)

Dice, L.R.: Measures of the amount of ecologic association between species. Ecology 26, 297--302 (1945)

work page 1945

[5] [5]

In: Proceedings of Machine Learning Research (PMLR)

Dusenberry, M., Jerfel, G., Wen, Y., Ma, Y., Snoek, J., Heller, K., Lakshminarayanan, B., Tran, D.: Efficient and scalable bayesian neural nets with rank-1 factors. In: Proceedings of Machine Learning Research (PMLR). pp. 2782--2792 (2020)

work page 2020

[6] [6]

Artificial Intelligence Review 56(1), 1513--1589 (2023)

Gawlikowski, J., Tassi, C.R., Ali, M., Lee, J.K., Humt, M., Feng, K., Kruspe, A., Triebel, R., Jung, P., Roscher, R., Shahzad, M., Yang, L., Bamler, R., Ng, A.Y., Kim, D., Meiner, J., Mikut, R., Heinrich, S., Weber, J., Brendel, M., Vogt, J.E.: A survey of uncertainty in deep neural networks. Artificial Intelligence Review 56(1), 1513--1589 (2023)

work page 2023

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Computers in biology and medicine 155, 106389 (2023)

Gong, H., Chen, J., Chen, G., Li, H., Li, G., Chen, F.: Thyroid region prior guided attention for ultrasound segmentation of thyroid nodules. Computers in biology and medicine 155, 106389 (2023)

work page 2023

[8] [8]

Huang, Y., Yang, X., Liu, L., Zhou, H., Chang, A., Zhou, X., Chen, R., Yu, J., Chen, J., Chen, C., Liu, S., Chi, H., Hu, X., Yue, K., Li, L., Grau, V., Fan, D.P., Dong, F., Ni, D.: Segment anything model for medical images? Medical Image Analysis 92, 103061 (2024)

work page 2024