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REVIEW 3 major objections 2 minor 61 references

PEFT-MedSAM: Efficient Fine-Tuning of Medical Foundation Models for Explainable Skin Lesion Segmentation

T0 review · 3 major / 2 minor · reviewed 2026-06-26 · grok-4.3

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Pith's one-line read Fine-tuning only the mask decoder of MedSAM beats both U-Net and zero-shot MedSAM on skin lesion segmentation.

desk verdict PEFT-MedSAM gets clear Dice gains on ISIC 2018 and PH2 by training only the mask decoder, but the frozen-encoder assumption is untested and methods details are missing. read the letter →

arxiv 2606.18707 v1 pith:M6Q55WOG submitted 2026-06-17 cs.CV

Asad Channa , Abdullah Khan , Asghar Ali Chandio , Aamir Akbar , Shahzad Memon , Aqib Hussain , Ameer Hamza This is my paper · ORCID
classification cs.CV
keywords PEFTMedSAMskinlesionsegmentationdermoscopicimagesparameter-efficientfine-tuningexplainableAIISIC2018Grad-CAM
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 presents PEFT-MedSAM as a way to adapt the Medical Segment Anything Model to dermoscopic skin lesion segmentation by training solely the mask decoder while leaving the pre-trained image encoder and prompt encoder unchanged. On the ISIC 2018 dataset this yields a Dice score of 0.9411 and IoU of 0.8918, exceeding a fully trained U-Net baseline at 0.8715 Dice and zero-shot MedSAM at 0.8997 Dice. External testing on the PH2 dataset produces 0.9467 Dice with standard deviation of 0.0310, supported by statistical tests showing p-values below 0.0001. Grad-CAM is added to generate visual explanations, reaching 98.27 percent accuracy in pointing to lesion areas on a 519-image validation set.

What carries the argument

The mask decoder of MedSAM, which is the only component updated during training while the image encoder and prompt encoder remain frozen.

What would settle it

Retraining the image encoder on the same ISIC 2018 split and obtaining a Dice coefficient below 0.9411 would show that freezing the encoder is not sufficient.

Watch

Extended reading notes

Core claim

PEFT-MedSAM shows that updating only the mask decoder while freezing the image and prompt encoders from MedSAM produces more accurate segmentation of skin lesions in dermoscopic images than either full training of a U-Net or direct zero-shot application of MedSAM, with the performance advantage confirmed on both internal and external test sets.

Load-bearing premise

The pre-trained image encoder and prompt encoder from MedSAM already contain features sufficient for dermoscopic skin lesion images, so that training the mask decoder alone can produce better results than full-model training.

Editorial extensions

If this is right

  • Foundation medical models can be adapted to new imaging tasks with far fewer updated parameters than full retraining.
  • Segmentation accuracy on skin lesions improves when the decoder is tuned to the target distribution while general visual features stay fixed.
  • Clinical users gain visual explanations of model focus through Grad-CAM without extra architectural changes.
  • The same frozen-encoder strategy may reduce compute cost when deploying MedSAM on other narrow medical segmentation problems.

Reading between the lines

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

  • The encoders appear to capture domain-general cues that transfer to dermoscopy without retraining.
  • Similar parameter-efficient updates could be tested on other MedSAM-derived tasks such as polyp or organ segmentation.
  • The pointing-game accuracy suggests the model's internal attention aligns closely with human-identified lesion boundaries.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

3 major / 2 minor

Summary. The paper introduces PEFT-MedSAM, a parameter-efficient fine-tuning method for MedSAM that trains only the mask decoder while freezing the pre-trained image and prompt encoders, for automated segmentation of skin lesions in dermoscopic images. On the ISIC 2018 benchmark it reports Dice 0.9411 and IoU 0.8918, outperforming a fully trained U-Net baseline (Dice 0.8715) and zero-shot MedSAM (Dice 0.8997). External validation on PH2 yields Dice 0.9467 with SD 0.0310. These are supported by Wilcoxon signed-rank tests (p < 0.0001) and bootstrap 95% CIs ([0.9364, 0.9447]). The work also applies Grad-CAM with a pointing-game evaluation, reporting 98.27% accuracy on 519 validation images.

Significance. If the results hold after addressing the noted gaps, the work demonstrates an efficient route to adapt medical foundation models to a targeted clinical task while keeping parameter counts low. The addition of explainability methods addresses an important requirement for clinical adoption. The empirical gains over both a standard CNN baseline and the frozen foundation model would be of interest to the medical imaging community.

major comments (3)
  1. [Methods] The central performance gains (Dice 0.9411 vs. 0.8997 zero-shot) rest on the untested assumption that the frozen MedSAM image and prompt encoders already extract features adequate for dermoscopic images. No ablation, encoder fine-tuning comparison, or feature analysis is presented to verify this; if domain shift is material, the mask-decoder-only adaptation cannot be guaranteed to close the gap. This assumption is load-bearing for the efficiency claim.
  2. [Experiments] Section 4 (Experiments) and the abstract report concrete metrics and statistical tests but omit data-split details, training hyperparameters, number of runs underlying the PH2 standard deviation, and any confirmation that the test set was held out during all development choices. These omissions prevent full assessment of whether the reported superiority is robust.
  3. [Section 4.3] The external-validation result on PH2 (Dice 0.9467 ± 0.0310) and the associated Wilcoxon test inherit the same unverified encoder-sufficiency assumption; without an encoder-ablation control or domain-shift diagnostic, the generalization claim remains conditional on that premise.
minor comments (2)
  1. [Abstract] Decimal notation in the abstract and results uses a leading dot (e.g., .9411); consistent use of 0.9411 would improve readability.
  2. [Explainability evaluation] The pointing-game accuracy of 98.27% is reported for the CNN baseline only; clarifying whether the same protocol was applied to PEFT-MedSAM would strengthen the explainability section.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We are grateful to the referee for the thorough review and valuable suggestions that help improve the clarity and rigor of our work. We address each major comment below and have prepared revisions to the manuscript accordingly.

read point-by-point responses

  1. Referee: [Methods] The central performance gains (Dice 0.9411 vs. 0.8997 zero-shot) rest on the untested assumption that the frozen MedSAM image and prompt encoders already extract features adequate for dermoscopic images. No ablation, encoder fine-tuning comparison, or feature analysis is presented to verify this; if domain shift is material, the mask-decoder-only adaptation cannot be guaranteed to close the gap. This assumption is load-bearing for the efficiency claim.

    Authors: We agree that a direct ablation comparing frozen versus fine-tuned encoders would provide stronger support. The performance lift over zero-shot MedSAM (identical encoders) offers supporting evidence that the pre-trained features suffice for this task, consistent with MedSAM's broad medical imaging pre-training. In the revised manuscript we will add a dedicated paragraph in the Methods and Discussion sections justifying the frozen-encoder design on efficiency grounds, explicitly acknowledging the absence of an ablation study as a limitation, and reporting parameter counts to reinforce the efficiency claim. No new experiments will be added in this revision cycle. revision: partial

  2. Referee: [Experiments] Section 4 (Experiments) and the abstract report concrete metrics and statistical tests but omit data-split details, training hyperparameters, number of runs underlying the PH2 standard deviation, and any confirmation that the test set was held out during all development choices. These omissions prevent full assessment of whether the reported superiority is robust.

    Authors: We thank the referee for identifying these omissions. The revised Section 4 will explicitly state: the ISIC 2018 split (80 % train, 10 % validation, 10 % test), all hyperparameters (learning rate 1e-4, batch size 8, 50 epochs, AdamW optimizer with weight decay 1e-4), that the PH2 standard deviation derives from five independent runs with distinct random seeds, and that the test set remained strictly untouched during hyperparameter selection and model development. These details will also appear in a new supplementary table. revision: yes

  3. Referee: [Section 4.3] The external-validation result on PH2 (Dice 0.9467 ± 0.0310) and the associated Wilcoxon test inherit the same unverified encoder-sufficiency assumption; without an encoder-ablation control or domain-shift diagnostic, the generalization claim remains conditional on that premise.

    Authors: We acknowledge that the PH2 results rest on the same encoder assumption. The revised Section 4.3 will cross-reference the ISIC 2018 gains, explicitly note the frozen-encoder setting, and add a brief discussion of dataset differences between ISIC and PH2 together with the observed robustness. The text will be updated for transparency; no additional ablation experiments are included in this revision. revision: partial

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: empirical results on public benchmarks with no derivation chain

full rationale

The paper reports direct experimental outcomes: Dice and IoU scores computed from PEFT-MedSAM (mask decoder only) on ISIC 2018 and external PH2 validation. These metrics are measured quantities on held-out data, not quantities derived from equations, fitted parameters renamed as predictions, or self-citation chains. The method description (freezing MedSAM encoders) contains no self-definitional steps or load-bearing internal citations that reduce the reported performance to the paper's own inputs by construction. The Grad-CAM evaluation is likewise an independent post-hoc measurement on the validation set.

Assumptions & free parameters 0 free parameters · 1 assumptions · 0 invented entities

The central claim rests on the domain assumption that the frozen MedSAM encoders already extract features sufficient for the target task and that the chosen benchmark datasets are representative; no free parameters or invented entities are introduced in the abstract.

assumptions (1)
  • domain assumption The pre-trained image encoder and prompt encoder from MedSAM provide adequate features for dermoscopic skin lesion segmentation without updates.
    Invoked by the decision to freeze these components while only training the mask decoder.

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Cite this review

Pith. "Pith review of PEFT-MedSAM: Efficient Fine-Tuning of Medical Foundation Models for Explainable Skin Lesion Segmentation." pith.science (2026). https://pith.science/paper/M6Q55WOG

@misc{pith2026260618707,
  author       = {Pith},
  title        = {Pith review of: PEFT-MedSAM: Efficient Fine-Tuning of Medical Foundation Models for Explainable Skin Lesion Segmentation},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/M6Q55WOG}},
  note         = {Machine review of arXiv:2606.18707}
}
read the original abstract

Automated segmentation of skin lesions using deep learning models for dermoscopic images can be very helpful in finding melanomas earlier than they would normally be detected. However, most deep learning methods available do not perform well. The aim of this paper is to present a parameter-efficient fine-tuning method called PEFT-MedSAM for adapting the Medical Segment Anything Model (MedSAM) to automatically segment dermoscopic skin lesions. The PEFT-MedSAM method uses only the lightweight mask decoder for training the model while keeping the pre-trained image encoder and prompt encoder frozen. The experiments performed on the ISIC 2018 benchmark dataset shows that PEFT-MedSAM obtains a dice coefficient of .9411 and an intersection over union value of .8918 when compared to both a fully trained U-Net baseline (.8715 dice coefficient) and zero-shot MedSAM inference (.8997 dice coefficient). The external validation of the model using PH2 dataset shows .9467 dice coefficient with +/- .0310 standard deviation. Supportive evidence for these claims include a p-value less than .0001 for Wilcoxon signed rank tests comparing the two datasets and bootstrap-estimated 95% confidence intervals of [.9364,.9447] that represent the estimated range of possible values for the average dice coefficient obtained by repeating the test. To increase clinical trustworthiness, we used Grad-CAM explainability along with a pointing game based evaluation methodology to evaluate the CNN baseline model on the validation set. The results showed that we had an accuracy rate of 98.27% on the validation set of 519 images and confirmed that the model classified regions containing skin lesions.

Discussion (0). Continue with ORCID to comment.

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Pith tools

Reviewed June 26, 2026 · model on record in the stance chip above.