FocusNet: Transformer-enhanced Polyp Segmentation with Local and Pooling Attention

Debesh Jha; Deepak Rajan Nayak; Jonas Varkey; Jun Zeng; KC Santosh; Thomas de Lange; Tyler Berzin

REVIEW 3 major objections 5 minor 34 references

FocusNet: Transformer-enhanced Polyp Segmentation with Local and Pooling Attention

T0 review · 3 major / 5 minor · reviewed 2026-08-16 · deepseek-v4-flash

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Pith's one-line read FocusNet, a Transformer-attention network, segments colon polyps across five imaging modalities and outperforms prior methods on four of them.

desk verdict Useful multi-modality benchmark and a decent architecture, but the 'consistently outperforms' claim is contradicted by the paper's own Table I, and the single-run statistics can't support the marginal gains. read the letter →

arxiv 2504.13597 v1 pith:R3FW3SLF submitted 2025-04-18 eess.IV cs.AIcs.CV

Jun Zeng , KC Santosh , Deepak Rajan Nayak , Thomas de Lange , Jonas Varkey , Tyler Berzin , Debesh Jha This is my paper · ORCID

classification eess.IVcs.AIcs.CV

keywords polypsegmentationcolonoscopymulti-modalityimagingtransformerattentionmechanismFocusNetDBmedicalimage

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 tries to establish that one network, FocusNet, can segment colorectal polyps accurately across the five endoscopic imaging modalities used in colonoscopy—white light (WLI), blue laser (BLI), flexible spectral imaging (FICE), linked color (LCI), and narrow band (NBI)—and across data from multiple clinical centers. If that holds, a single model could serve in real screening environments instead of being retrained for each scope setting. FocusNet builds a pyramid vision transformer encoder with three added modules: two cross-semantic decoders that fuse deep features into coarse maps, a detail-enhancement module that sharpens shallow edges, and a focus attention module that mixes local and pooled global context. On the PolypDB benchmark the paper reports dice scores of 82.47% (BLI), 88.46% (FICE), 92.04% (LCI), 82.09% (NBI), and 93.42% (WLI), with FocusNet leading on all modality-wise tests except WLI, where it trails PVT-CASCADE by 0.41 points.

What carries the argument

The load-bearing mechanism is the Focus Attention Module (FAM), a dual-path attention block that balances local detail with global context: it builds a shared query from the detail-enhanced shallow features, derives separate key/value pairs from local windows and from adaptive average pooling, concatenates the two similarity scores before a single softmax, and adds the local and pooled attention outputs before multiplying with a channel-attention-refined feature. Two Cross-semantic Interaction Decoder Modules (CIDM-M and CIDM-A) supply the coarse segmentation maps and fused features by combining deep encoder levels with element-wise multiplication or addition plus channel/spatial attention. The Detail Enhancement Module (DEM) processes shallow features through four parallel branches of 1×3 and 3×1 deformable convolutions followed by efficient channel attention. The network's final prediction is the element-wise sum of four segmentation maps, two from the decoders and two from the shared FAM.

What would settle it

Retrain FocusNet and the strongest baselines several times with different random seeds on the same PolypDB splits; if the seed-to-seed spread in dice exceeds 0.59 percentage points, the smallest reported winning margin, the claim that FocusNet consistently outperforms is not established.

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Extended reading notes

Core claim

FocusNet's central claim is that combining coarse semantic maps with boundary-focused shallow features, then reweighting them through a focus attention module, yields a polyp segmenter that generalizes across modalities and centers better than existing CNN-, transformer-, and mamba-based models. The architecture uses a pyramid vision transformer backbone; two cross-semantic interaction decoder variants (multiplicative CIDM-M and additive CIDM-A) assemble deep feature maps into coarse predictions, while the detail enhancement module passes the first-stage features through four branches of deformable convolutions and channel attention. The focus attention module then computes a shared query and separate local and pooling keys/values, concatenates the two similarity scores before softmax, and combines the attended values with channel-attended input. The paper reports this design produces the highest dice coefficient on BLI, FICE, LCI, and NBI in the modality-wise protocol, and on WLI it is competitive but slightly below PVT-CASCADE; the same pattern appears in center-wise tests, which the paper attributes to pooling attention sacrificing some fine detail.

Load-bearing premise

The load-bearing premise is that the reported dice differences come from the models' architectures rather than from luck in a single training run, because the paper reports one run per model with early stopping and no confidence intervals.

Editorial extensions

If this is right

A single FocusNet model trained on WLI can be applied to BLI, FICE, LCI, and NBI images without modality-specific fine-tuning, the precondition for realistic multi-modality colonoscopy deployment.
On the modality-wise benchmark, FocusNet's largest gains come on the hardest, smallest test sets (BLI and NBI), where it beats the prior best by 4.2 and 5.6 percentage points of dice.
The WLI and center-wise results show FocusNet does not dominate every setting: PVT-CASCADE retains the top WLI dice by 0.41 points and leads on all three center-wise tests, so the paper's 'consistently outperforms' phrasing holds only for four of five modality tests.
Ablations on BLI show the three modules are additive: the full model reaches 0.8247 dice versus 0.8170 for the backbone plus CIDM-M alone, so each component earns its place.

Reading between the lines

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

A further step, not in the paper: if the multi-modality result replicates, PolypDB-style multi-modality benchmarks should become the default for polyp segmentation, because single-modality WLI leaderboards cannot detect whether a model will survive a modality shift in the clinic.
The FAM's local and pooling window sizes are set to 3 and the paper says they are adjustable; a natural next experiment is to sweep window sizes and pooling strides to see whether the small WLI and center-wise gap to PVT-CASCADE closes or widens.
A fair reading of the reported p-values against PVT-CASCADE on WLI and the Simula center (0.34 and 0.21) is that those differences may be within noise; a multi-seed evaluation with confidence intervals would settle whether FocusNet truly matches or trails on those splits.

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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

Hi — quick read of FocusNet. Short version: the PolypDB multi-modality evaluation is the most useful thing here, and FocusNet is a reasonable engineering contribution. But the abstract overstates the results, and the statistics as reported aren't enough to support the headline.

What's actually new: first multi-modality, multi-center polyp segmentation benchmark on PolypDB, with five modalities and three centers, and a public code release. The architecture combines PVTv2 with a detail-enhancement module using deformable convolutions and a local-plus-pooling attention module. That is incremental—FAM is close to TransNeXt's foveal attention—but the combination is new and it performs well on four of five modalities.

Credit where due: the experimental setup is broad, the comparison includes CNN, transformer, and Mamba baselines, and the failure-case discussion is honest. They also acknowledge in Section III.C that they are slightly behind PVT-CASCADE on WLI, which is more than some papers do.

The problems. First, the abstract and conclusion say 'consistently outperforms,' but Table I shows FocusNet loses to PVT-CASCADE on WLI by 0.41% mDSC, and Table II shows losses on BKAI and Simula centers. So the central claim is not consistent. That should be fixed. Second, all results are single runs, early stopping, and test sets as small as 60 images. The p-values in the tables don't state which test, how many runs, or any variance. A 0.41% gap on a 60-image set is easily seed noise. The ablation is single-run on BLI, so the claimed DEM gain of 1.06% mDSC is also within plausible noise. Third, there is a self-comparison with RMAMamba-T, a method from their own group, which is fine but should be disclosed more clearly.

None of this makes the paper worthless. The benchmark is useful, the code is public, and the model is competitive. But the claims need to be scaled back and the statistics need to be redone with multiple seeds and specified tests.

For peer review: yes, send it. A serious referee can get this into good shape, but as it stands the headline is not supported.

Referee Report

3 major / 5 minor

Summary. The paper proposes FocusNet, a Transformer-enhanced polyp segmentation network built on a PVTv2 backbone, with three new modules: a Cross-semantic Interaction Decoder Module (CIDM) in multiplicative and additive variants, a Detail Enhancement Module (DEM) using multi-branch deformable convolutions on shallow features, and a Focus Attention Module (FAM) combining local and pooling attention. The method is evaluated on PolypDB, a multi-center, multi-modality colonoscopy dataset (BLI, FICE, LCI, NBI, WLI), in both modality-wise and center-wise settings, and is compared against seven CNN-, Transformer-, and Mamba-based baselines. The paper reports top mDSC values on four modality-wise test splits (BLI 82.47%, FICE 88.46%, LCI 92.04%, NBI 82.09%) and competitive results on WLI (93.42%), along with an ablation study, computational complexity comparison, qualitative examples, and failure-case analysis. The central claim is that FocusNet consistently outperforms prior state-of-the-art methods across all five modalities and demonstrates strong generalization across centers.

Significance. If the reported results are robust, FocusNet would be a useful contribution to multi-modality polyp segmentation, a clinically relevant direction given the limited modality coverage of most existing datasets and models. The paper's strengths include the use of a newly assembled multi-center, multi-modality dataset, evaluation under both modality-wise and center-wise protocols, inclusion of efficiency metrics, qualitative results, failure-case discussion, and a public code link. However, the significance of the paper's central claim is weakened by the absence of repeated-run statistics and by the fact that the reported numbers themselves contradict the claimed 'consistent' superiority on several settings. The architectural ideas, especially the combination of local and pooling attention in FAM, are reasonable and potentially impactful, but the current evidence does not fully support the strength of the stated conclusions.

major comments (3)

[Abstract, Section III.C, Tables I and II] The claim that FocusNet 'consistently outperforms existing state-of-the-art approaches' is not supported by the reported numbers. On WLI, FocusNet has mDSC 0.9342 versus PVT-CASCADE 0.9383 and mIoU 0.8862 versus 0.8927; on BKAI, mDSC is 0.9127 versus PVT-CASCADE 0.9138; on Simula, mDSC is 0.9230 versus PVT-CASCADE 0.9306. Section III.C itself acknowledges that FocusNet is 'slightly inferior' on WLI and on BKAI/Simula. The abstract, contributions list, and conclusion should be revised to state that FocusNet outperforms the compared methods on BLI, FICE, LCI, and NBI, and is competitive on WLI and across centers.
[Section III.A and Tables I, II, IV] The manuscript lists P-values in Tables I and II but never specifies the statistical test, the number of runs, or the variance structure; all metrics appear to come from a single training run with early stopping. Test sets are small (BLI 70, FICE 70, LCI 60, NBI 146, Karolinska 30), and the smallest reported gaps are 0.41% mDSC on WLI and 0.11% mDSC on BKAI. To support the claim of statistical significance made in Section IV, the authors should report mean and standard deviation over multiple seeds, provide confidence intervals or bootstrap estimates, and name the test used.
[Section III.D, Table IV] The ablation study is performed on a single modality (BLI) with single-run metrics. The DEM contribution is +1.06% mDSC over Setting #5, while Setting #5 itself is 0.33% mDSC below Setting #3, so the incremental benefit of adding DEM and the second CIDM variant is not clearly established. Without repeated runs or an additional validation modality, the claim that each module contributes to the final result is not supported by the present evidence.

minor comments (5)

[II.D, Eq. (10)] Equation (10) has an unbalanced parenthesis: the right-hand side should be P + Conv_{1x1}(Conv_{3x3}(Conv_{3x3}(O_f))) or the intended structure should be made explicit.
[Table II caption] The caption cites reference [34] (Tajbakhsh et al.) for the PolypDB dataset, but the dataset is introduced in reference [27]; the citation should be corrected.
[III.C.1] In the paragraph describing NBI results, the text says 'FocusNet outperforms G-CASCADE by approximately 5.62%, 5.58%, and 4.64%' but no method named G-CASCADE appears elsewhere; this appears to be a typo for PVT-CASCADE.
[II.C, after Eq. (5)] The phrase 'Eficient Channel Attention' should be 'Efficient Channel Attention'.
[III.A] In the center-wise description, 'for it’s common in all three centers' should be revised to 'since it is common to all three centers'.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: FocusNet's reported metrics are measured on held-out PolypDB test splits, not derived from fitted parameters or self-citations.

full rationale

The paper's central claim is an empirical architecture comparison. Section II defines the CIDM, DEM, and FAM modules via convolutional and attention operations, but none of these equations outputs a dice or mIoU number from a fitted parameter. Tables I, II, and IV report measured metrics on PolypDB test splits (BLI 70, FICE 70, LCI 60, NBI 146, WLI 360, BKAI 1000, Karolinska 30, Simula 257), computed from predictions against expert masks. These metrics are not derived from the model definition, so no fitted input is renamed as a prediction. Self-citations to PolypDB [27] and RMAMamba-T [33] exist, but they are not load-bearing in a logical sense: PolypDB is a fixed dataset with expert annotations, and RMAMamba-T is one of several baselines re-run under the same protocol. The paper's own admission in Section III.C that FocusNet is 'slightly inferior' to PVT-CASCADE on WLI (mDSC 0.9342 vs 0.9383) and on BKAI/Simula contradicts the abstract's 'consistently outperforms' wording, but inconsistency is not circularity. Similarly, the lack of confidence intervals or multiple seeds is a statistical robustness concern, not a definitional reduction. No uniqueness theorem is imported from the authors' prior work, no ansatz is smuggled in via citation, and no known empirical result is renamed as a new derivation. The derivation chain is self-contained; the only concerns are experimental rigor and overclaiming, which are outside the circularity definition.

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

The paper's central claim is empirical, so the ledger contains no invented entities. The main free parameters are hand-set architecture hyperparameters. The axioms are standard deep learning assumptions about the dataset and evaluation protocol.

free parameters (4)

FAM window size = 3
Both local and pooling window sizes are set to 3 by hand (Section II.D), controlling the attention granularity.
DEM branch count = 4
The shallow feature is split into k=4 branches in DEM (Eq. 4), chosen by hand.
FAM input channels = 32
The feature map T' is transformed into 32 channels before entering FAM (Section II.D).
Training hyperparameters = lr=1e-4, batch=16, epochs=500, patience=50
Set in Section III.B; these affect the results but are standard choices.

assumptions (3)

domain assumption The PolypDB dataset masks are accurate, expert-verified ground truth.
The paper relies on PolypDB annotations for all metrics (Section III.A).
domain assumption Training only on WLI and testing on other modalities measures cross-modality generalization.
This is the evaluation protocol in Section III.A.
domain assumption Dice and IoU are valid measures of clinical polyp segmentation quality.
Used as primary evaluation metrics (Section III.A).

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Cite this review

Pith. "Pith review of FocusNet: Transformer-enhanced Polyp Segmentation with Local and Pooling Attention." pith.science (2026). https://pith.science/paper/R3FW3SLF

@misc{pith2026250413597,
  author       = {Pith},
  title        = {Pith review of: FocusNet: Transformer-enhanced Polyp Segmentation with Local and Pooling Attention},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/R3FW3SLF}},
  note         = {Machine review of arXiv:2504.13597}
}

read the original abstract

Colonoscopy is vital in the early diagnosis of colorectal polyps. Regular screenings can effectively prevent benign polyps from progressing to CRC. While deep learning has made impressive strides in polyp segmentation, most existing models are trained on single-modality and single-center data, making them less effective in real-world clinical environments. To overcome these limitations, we propose FocusNet, a Transformer-enhanced focus attention network designed to improve polyp segmentation. FocusNet incorporates three essential modules: the Cross-semantic Interaction Decoder Module (CIDM) for generating coarse segmentation maps, the Detail Enhancement Module (DEM) for refining shallow features, and the Focus Attention Module (FAM), to balance local detail and global context through local and pooling attention mechanisms. We evaluate our model on PolypDB, a newly introduced dataset with multi-modality and multi-center data for building more reliable segmentation methods. Extensive experiments showed that FocusNet consistently outperforms existing state-of-the-art approaches with a high dice coefficients of 82.47% on the BLI modality, 88.46% on FICE, 92.04% on LCI, 82.09% on the NBI and 93.42% on WLI modality, demonstrating its accuracy and robustness across five different modalities. The source code for FocusNet is available at https://github.com/JunZengz/FocusNet.

Figures

Figures reproduced from arXiv: 2504.13597 by the authors.

**Figure 1.** Overview of the proposed FocusNet architecture. The network comprises four main components: (1) a PVTv2 [22] backbone that extracts multi-scale hierarchical features from the input image; (2) a Detail Enhancement Module (DEM) that refines shallow features (f1) to preserve fine-grained boundary information; (3) two Crosssemantic Interaction Decoder Modules (CIDM-M and CIDM-A) that process deep features (f2, f3, f4) … view at source ↗

**Figure 2.** Samples from various modalities in the PolypDB dataset are shown in each row, with the corresponding modality descriptions provided on the left of each row. P ′ = P + Conv1×1(Conv3×3(Conv3×3(Of )) (10) where ⊙ denotes hadamard product. Here, both Fˆ 1 from CIDM-M and Fˆ 2 from CIDM-A are fed into the shared FAM module to generate the corresponding segmentation maps. III. Experiments A. Datasets and Evaluation Metri… view at source ↗

**Figure 3.** Qualitative results of different methods across BLI and NBI modalities. It is obvious that FocusNet produces better segmentation masks, particularly in challenging scenarios involving small, flat, multiple polyps. Models such as RMAMamba-T showed under-segmentation whereas our model provided accurate and robust segmentation [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗

Figures from the paper (3 more)

**Figure 4.** Figure 4: Qualitative results of different methods across FICE, LCI, and WLI modalities. Here, most of the advanced models (PVT-Cascade and RMAMamba-T) can detect polyps, however, FocusNet produces segmentation masks with higher boundary precision and consistency. center-wise…

**Figure 5.** Figure 5: Visualization of intermediate feature maps from our model. Columns from left to right: input image, ground truth mask (GT), predicted mask (Ours), encoder feature maps (f1, f2), detail-enhanced transformer map (T ′ ), and decoder outputs (Fˆ 1, Fˆ 2). Here, the shallo…

**Figure 6.** Figure 6: Qualitative visualization of some failure cases. integrating boundary information from shallow features and has achieved superior segmentation performance. Both the local and pooling window sizes can be adjusted to optimize the model for specific tasks. The model achie…

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Pith tools

Reviewed August 16, 2026 · model on record in the stance chip above.

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Doubleu-net: A deep convolutional neural network for medical image segmentation,

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Transunet: Transformers make strong encoders for medical image segmentation,

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arXiv 2021