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arxiv: 2604.17865 · v1 · submitted 2026-04-20 · 💻 cs.CV

Sharpening Lightweight Models for Generalized Polyp Segmentation: A Boundary Guided Distillation from Foundation Models

Shivanshu Agnihotri , Snehashis Majhi , Deepak Ranjan Nayak This is my paper

Pith reviewed 2026-05-10 05:00 UTC · model grok-4.3

classification 💻 cs.CV
keywords polyp segmentationknowledge distillationlightweight modelsboundary guidancevision foundation modelsmedical image segmentation
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The pith

LiteBounD distills boundary and semantic priors from foundation models into lightweight polyp segmenters.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper introduces LiteBounD to improve compact models such as U-Net for polyp segmentation by transferring knowledge from large vision foundation models like SAM and DINOv2. It targets the problems of weak boundaries, appearance variations, and limited data that hinder lightweight models while avoiding the high cost of running foundation models directly. The framework uses dual-path distillation to separate semantic and boundary representations, frequency-aware alignment to handle global and detail information separately, and a boundary-aware decoder to combine them for accurate output. Experiments on both familiar and new datasets show the distilled models outperform their baselines and reach near state-of-the-art results while staying efficient enough for real-time use.

Core claim

LiteBounD transfers complementary semantic and structural priors from multiple vision foundation models into compact segmentation backbones via a dual-path distillation mechanism that disentangles semantic and boundary-aware representations, a frequency-aware alignment strategy that supervises low-frequency global semantics and high-frequency boundary details separately, and a boundary-aware decoder that fuses multi-scale encoder features with distilled semantically rich boundary information for precise segmentation.

What carries the argument

LiteBounD framework with dual-path distillation, frequency-aware alignment, and boundary-aware decoder that transfers priors from VFMs to lightweight models.

Load-bearing premise

That the dual-path distillation, frequency-aware alignment, and boundary-aware decoder can transfer useful priors from VFMs to lightweight models despite domain mismatch without introducing artifacts that degrade segmentation on unseen data.

What would settle it

A test showing LiteBounD fails to beat its lightweight baselines on unseen datasets such as ColonDB or ETIS, or produces visible boundary artifacts in clinical images.

Figures

Figures reproduced from arXiv: 2604.17865 by Deepak Ranjan Nayak, Shivanshu Agnihotri, Snehashis Majhi.

Figure 1
Figure 1. Figure 1: Overall architecture of the proposed LiteBounD framework. . F −1 BoundLFF = IFFT(MLLFF, Ffreq ba ), F −1 BoundHFF = IFFT(MHHFF, Ffreq ba ) (6) To enable feature-level distillation, these high- and low￾frequency features are mapped back to the spatial domain us￾ing a 2D inverse FFT. The resulting distillation-ready features F −1 LFF, F −1 HFF, F −1 BoundLFF, and F −1 BoundHFF, are then injected into the bas… view at source ↗
Figure 2
Figure 2. Figure 2: Qualitative comparison on seen and unseen datasets. [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
read the original abstract

Automated polyp segmentation is critical for early colorectal cancer detection and its prevention, yet remains challenging due to weak boundaries, large appearance variations, and limited annotated data. Lightweight segmentation models such as U-Net, U-Net++, and PraNet offer practical efficiency for clinical deployment but struggle to capture the rich semantic and structural cues required for accurate delineation of complex polyp regions. In contrast, large Vision Foundation Models (VFMs), including SAM, OneFormer, Mask2Former, and DINOv2, exhibit strong generalization but transfer poorly to polyp segmentation due to domain mismatch, insufficient boundary sensitivity, and high computational cost. To bridge this gap, we propose \textit{\textbf{LiteBounD}, a \underline{Li}gh\underline{t}w\underline{e}ight \underline{Boun}dary-guided \underline{D}istillation} framework that transfers complementary semantic and structural priors from multiple VFMs into compact segmentation backbones. LiteBounD introduces (i) a dual-path distillation mechanism that disentangles semantic and boundary-aware representations, (ii) a frequency-aware alignment strategy that supervises low-frequency global semantics and high-frequency boundary details separately, and (iii) a boundary-aware decoder that fuses multi-scale encoder features with distilled semantically rich boundary information for precise segmentation. Extensive experiments on both seen (Kvasir-SEG, CVC-ClinicDB) and unseen (ColonDB, CVC-300, ETIS) datasets demonstrate that LiteBounD consistently outperforms its lightweight baselines by a significant margin and achieves performance competitive with state-of-the-art methods, while maintaining the efficiency required for real-time clinical use. Our code is available at https://github.com/lostinrepo/LiteBounD.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

1 major / 4 minor

Summary. The manuscript proposes LiteBounD, a lightweight boundary-guided distillation framework that transfers semantic and structural priors from vision foundation models (VFMs such as SAM, OneFormer, Mask2Former, and DINOv2) to compact backbones (U-Net, U-Net++, PraNet) for polyp segmentation. The method consists of a dual-path distillation mechanism to disentangle semantic and boundary-aware representations, a frequency-aware alignment strategy that separately supervises low-frequency global semantics and high-frequency boundary details, and a boundary-aware decoder that fuses multi-scale features with the distilled boundary information. Experiments on seen datasets (Kvasir-SEG, CVC-ClinicDB) and unseen datasets (ColonDB, CVC-300, ETIS) are reported to show consistent outperformance over lightweight baselines, competitiveness with state-of-the-art methods, and retention of real-time efficiency. Code is released at https://github.com/lostinrepo/LiteBounD.

Significance. If the reported gains hold under rigorous verification, the work would offer a practical route to deploying accurate, generalizable polyp segmentation in clinical environments by combining the efficiency of lightweight models with the broad priors of VFMs. The explicit handling of boundary sensitivity and frequency separation directly targets known failure modes in medical segmentation (weak boundaries, appearance variation, domain shift). The inclusion of unseen-dataset evaluation and the release of reproducible code strengthen the contribution and facilitate follow-up research.

major comments (1)
  1. [Experiments] Experiments section: the central claim of consistent outperformance and generalization rests on quantitative results, yet the manuscript provides no ablation tables isolating the contribution of the frequency-aware alignment component versus the dual-path distillation on the unseen datasets (ColonDB, CVC-300, ETIS). Without these controls it remains unclear whether the full framework is required for the reported gains or whether simpler distillation suffices.
minor comments (4)
  1. [Abstract] Abstract: the LiteBounD acronym is introduced with underlined letters; this formatting is non-standard and should be replaced with conventional bold or italic styling for clarity and compatibility.
  2. [Method] Method: the boundary-aware decoder fusion step is described at a high level; adding an equation or pseudocode for the multi-scale feature combination would improve reproducibility.
  3. Figure captions: several figures lack explicit axis labels or metric definitions (e.g., Dice, IoU, HD95); ensure all quantitative plots are self-contained.
  4. [References] References: confirm that the most recent versions of the cited VFMs (SAM, DINOv2) are referenced with complete bibliographic details.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the constructive feedback and positive recommendation. We address the major comment below and will revise the manuscript accordingly.

read point-by-point responses

  1. Referee: Experiments section: the central claim of consistent outperformance and generalization rests on quantitative results, yet the manuscript provides no ablation tables isolating the contribution of the frequency-aware alignment component versus the dual-path distillation on the unseen datasets (ColonDB, CVC-300, ETIS). Without these controls it remains unclear whether the full framework is required for the reported gains or whether simpler distillation suffices.

    Authors: We appreciate this observation. The manuscript includes ablation studies on the seen datasets (Kvasir-SEG and CVC-ClinicDB) that isolate the contributions of the dual-path distillation and frequency-aware alignment components (Section 4.3, Table 4). However, equivalent component-wise ablations were not reported for the unseen datasets. To address the concern and strengthen the generalization evidence, we will add these ablation results for ColonDB, CVC-300, and ETIS in the revised manuscript. This will show that the full framework is required for the observed gains. revision: yes

Circularity Check

0 steps flagged

No significant circularity; derivation is self-contained

full rationale

The paper proposes LiteBounD as a new distillation framework whose core components (dual-path distillation, frequency-aware alignment, boundary-aware decoder) are introduced as independent design choices to transfer VFM priors to lightweight backbones. No equations, predictions, or uniqueness claims reduce by construction to fitted inputs or prior self-citations. Performance assertions rest on experimental results across multiple datasets rather than any self-referential derivation. This matches the expected non-circular outcome for a standard methodological contribution.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 0 invented entities

The framework rests on standard knowledge-distillation assumptions and the premise that foundation models encode transferable polyp-relevant features; no new entities are postulated.

free parameters (1)
  • distillation loss balancing weights
    Hyperparameters that trade off semantic versus boundary supervision are typically selected or tuned during training.
axioms (1)
  • domain assumption Vision foundation models contain complementary semantic and structural priors that can be transferred to polyp segmentation despite domain shift.
    This premise underpins the entire distillation approach and is stated in the motivation section of the abstract.

pith-pipeline@v0.9.0 · 5628 in / 1154 out tokens · 51045 ms · 2026-05-10T05:00:58.761716+00:00 · methodology

discussion (0)

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Reference graph

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