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REVIEW 4 major objections 5 minor 41 references

DPE-Net: Dual-Parallel Encoder Based Network for Semantic Segmentation of Polyps

T0 review · 4 major / 5 minor · reviewed 2026-08-12 · deepseek-v4-flash

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Pith's one-line read The paper claims that DPE-Net, a two-branch encoder network combining dual and single convolution blocks with depth-wise concatenation, outperforms six established deep-learning models for polyp segmentation on Kvasir and CVC-ClinicDB…

desk verdict A plausible lightweight dual-branch encoder idea, but the paper's own Table II contains impossible metric values and contradicts the claimed superiority, so the empirical case collapses. read the letter →

arxiv 2412.00888 v2 pith:IBM6BWKT submitted 2024-12-01 eess.IV cs.CV

Malik Abdul Manan , Feng Jinchao , Shahzad Ahmed , Abdul Raheem This is my paper · ORCID
classification eess.IVcs.CV
keywords polypsegmentationdualparallelencodersemanticcolonoscopyimagesdeeplearningKvasir-SEGCVC-ClinicDBresidual
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

DPE-Net is a polyp segmentation architecture built from two encoder branches running in parallel: one uses dual convolution blocks to carry feature information through deeper layers, and the other uses single convolution blocks with a residual identity connection to preserve the input-like character of features. The branch outputs are merged by depth-wise concatenation before each transpose-convolution up-sampling stage, followed by a sigmoid pixel classifier. The paper's central claim is that this two-branch design reaches higher mean Dice and mean IoU than six established deep-learning models on Kvasir and CVC-ClinicDB, with mDice/mIoU of 0.919/0.866 on Kvasir and 0.931/0.891 on CVC-ClinicDB. It also claims the gain comes from combining the two branches, since an ablation shows either branch alone scores far lower. A sympathetic reader would care because the model uses only 3.4 million parameters and trains in about six hours on consumer hardware, which suggests a lightweight alternative for a clinically relevant segmentation task.

What carries the argument

The central mechanism is the dual-branch encoder. Branch A is the dual convolution block: 1x1 and 3x3 convolutions with ReLU and batch normalization, producing a residual update $I_{k+1} = M + S$, where $M$ is the mapped feature input and $S$ is a 1x1-convolution/batch-norm projection that aligns channel counts. Branch B is the single convolution identity block: a 3x3 convolution with ReLU and batch normalization plus an element-wise skip, written $I_{k+1} = F_{I_1} + I_1$, where $I_1$ is the input feature map and $F_{I_1}$ its convolved version. The two branches are fused with depth-wise concatenation before each transpose-convolution layer, and the final segmentation comes from a sigmoid classifier. The pair of update rules is what the paper says maintains feature information over depth while adding diversity, and the ablation results are the evidence that the combination, not either branch alone, carries the performance.

What would settle it

Retrain every baseline on the exact same 80/10/10 splits of Kvasir and CVC-ClinicDB with the same loss, input size, and metric code, and check that each reported pair obeys $\mathrm{IoU} \le \mathrm{Dice}$; if Polyp-PVT or MEGANet then matches or exceeds DPE-Net's mDice/mIoU, or if the Deeplabv3+ Kvasir row cannot be reproduced, the central claim of superiority would be contradicted.

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Extended reading notes

Core claim

On the paper's own terms, DPE-Net establishes that running a dual-convolution encoder branch and a single-convolution residual branch in parallel, then merging them depth-wise before up-sampling, yields the best quantitative segmentation performance among the methods compared. The reported numbers are mDice 0.919 and mIoU 0.866 on Kvasir, and mDice 0.931 and mIoU 0.891 on CVC-ClinicDB, ahead of UNet, SegNet, Deeplabv3+, PraNet, Polyp-PVT, and MEGANet. The ablation study backs the design story: the dual-convolution branch alone gives mDice 0.640 on Kvasir and 0.552 on CVC-ClinicDB, the single-convolution branch alone gives 0.632 and 0.475, and the combined network with learning rate $10^{-3}$ jumps to 0.901 and 0.898 before the final configuration reaches 0.919 and 0.931. The authors read this as evidence that balancing depth-rich feature extraction with identity-preserving feature extraction is what drives the improvement.

Load-bearing premise

The load-bearing premise is that the baseline scores in Table II were produced under the same data split and evaluation protocol as DPE-Net; the paper does not state that the baselines were retrained on the same 80/10/10 split, so if those numbers came from different protocols the reported superiority over them would not be established.

Editorial extensions

If this is right

  • If the reported comparison holds, DPE-Net is a lighter alternative to the six compared models, using about 3.4 million parameters while posting the highest mDice and mIoU on both datasets.
  • The ablation indicates that deleting either branch is costly, so the parallel combination itself is the source of the gain; designs that keep both streams should generalize better than choosing one block type.
  • The same dual/single encoder pattern can be dropped into other encoder-decoder architectures at the pre-transpose fusion point, since the blocks are not specific to polyp datasets.
  • A 40-epoch SGDM schedule, 384x288 input size, and mixed-precision training give a reproducible six-hour training budget on an RTX 3060, which is practical for clinical data pipelines.

Reading between the lines

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

  • An implication the authors leave implicit: the dual and single branches may be learning complementary feature types, so inspecting where each branch fires on polyp boundaries versus interior could motivate fusion rules beyond depth-wise concatenation.
  • A testable extension the paper does not run: pairing the same encoder with attention or transformer decoders, which are standard options in current polyp segmentation work, to see whether the encoder's gains persist.
  • The reported 3.4M-parameter size and six-hour training time point toward real-time clinical use, but the paper does not report inference latency, so measuring frames-per-second would settle that implicit promise.
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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 / 5 minor

Summary. The paper proposes DPE-Net, a dual-parallel encoder network for polyp segmentation. One encoder branch uses dual convolution blocks with a residual-style mapping, and the other uses single convolution blocks with identity-style residual connections; the two branches are merged by depth-wise concatenation before transpose-convolution upsampling. Experiments are reported on Kvasir and CVC-ClinicDB, with an ablation study (Table I) and a comparison against UNet, SegNet, DeepLabv3+, PraNet, Polyp-PVT, and MEGANet (Table II). The authors claim the highest mDice and mIoU on both datasets, with 3.4 million parameters and 6-hour training on an RTX 3060.

Significance. If the reported results were reliable, a 3.4M-parameter encoder with parallel dual and single convolution branches would be a useful lightweight alternative for polyp segmentation, and the public-dataset evaluation would be a practical contribution. The paper also includes an ablation study isolating the two encoder branches, which is a positive feature. However, the central quantitative evidence is compromised: Table II contains a mathematically impossible metric pair and directly contradicts the stated superiority claim on CVC-ClinicDB. Because the main contribution is the claimed state-of-the-art performance, these problems are load-bearing and prevent the paper from being accepted in its current form.

major comments (4)
  1. [Section III-B, Table II] The DeepLabv3+ row on Kvasir reports mDice = 0.899 and mIoU = 0.915. For any binary segmentation, Dice = 2*IoU/(1+IoU), so IoU is always less than or equal to Dice. These two reported values cannot both be correct for the same set of predictions, indicating that the baseline numbers were not produced under a single consistent evaluation protocol. This invalidates the comparison basis for the entire table.
  2. [Abstract and Section III-B, Table II] The paper's central claim of 'superior performance' and 'highest mDice and mIoU values' is contradicted by its own Table II: on CVC-ClinicDB, Polyp-PVT is reported with mDice = 0.937, which exceeds DPE-Net's mDice = 0.931. The claimed advantage over Polyp-PVT on Kvasir is only 0.002 in mDice and 0.002 in mIoU, which is within run-to-run variation, yet no error bars, standard deviations, or statistical tests are reported anywhere in the manuscript.
  3. [Section III-B] The manuscript does not specify whether the baseline models were retrained on the same 80/10/10 split with the same preprocessing and image resolution (384x288) or whether their numbers were copied from prior publications. The inconsistent metric pair in Table II suggests that at least some baseline values come from incompatible protocols. Without a standardized experimental setup applied to all methods, the claimed superiority of DPE-Net is not established.
  4. [Section III-C, Table I] The ablation study reports that the proposed DPE-Net achieves accuracy 0.971 on Kvasir and 0.971 on CVC-ClinicDB, while Network 3 (both parallel blocks with LR 10^-3) achieves accuracy 0.921 and 0.918. The only stated difference between Network 3 and DPE-Net appears to be the learning rate (10^-3 vs 1e-4, described in Section III), but this is not explained in the ablation discussion, leaving the source of the substantial accuracy gain unclear.
minor comments (5)
  1. [Section III-A, Eqs. (3)-(4)] Equations (3) and (4) are garbled in the text; the definitions of Dice and IoU should be written with clear notation for true positives, false positives, and false negatives. The current rendering is not readable.
  2. [Introduction] The introduction cites 'MSRF-Net [28]' and 'Polyp-PVT [28]' with the same reference number; reference [28] is listed as Polyp-PVT only, so the MSRF-Net citation appears to be an error.
  3. [Section III-B, Figures 3-4] The paper refers to visual comparisons in Figures 3 and 4, but the figures as provided are largely unreadable in the manuscript text, with missing or broken image placeholders. The visual evidence cannot be assessed.
  4. [Section II] The architectural description is incomplete: the number of layers, kernel sizes for each stage, channel widths, the exact placement of batch normalization and max-pooling, and the decoder structure after the transpose convolution are not specified with sufficient detail for reproduction.
  5. [Section III] The paper states 'the datasets were split using an 80-10 ratio' and then says 'the remaining 10% was used for validation purposes,' which is ambiguous (80/10/10 presumably). The sentence should be clarified.

Circularity Check

0 steps flagged · score 1.0 of 10

No circular derivation: DPE-Net's reported results are evaluated against external baselines, and self-citations are contextual rather than load-bearing.

full rationale

The paper's central claim is an empirical comparison: DPE-Net achieves Dice 0.919 and mIoU 0.866 on Kvasir, and Dice 0.931 and mIoU 0.891 on CVC-ClinicDB, against UNet, SegNet, Deeplabv3+, PraNet, Polyp-PVT, and MEGANet. The proposed dual-parallel encoder architecture is described through residual-style equations (Eq. 1 and Eq. 2) that combine convolution outputs and identity additions; these are architectural definitions, not predictions derived from the evaluation metrics. The performance numbers are not obtained by fitting a parameter to the claimed outputs and then renaming that fit as a prediction. References to earlier works by the same group appear in the reference list and in broad medical-image-analysis context, but none is invoked as an authority that forces the DPE-Net design or forbids alternatives. There is no imported uniqueness theorem, no ansatz smuggled through a self-citation, and no known result renamed as a new contribution. The more serious issue in the paper is internal consistency of Table II: Deeplabv3+ is listed on Kvasir with mIoU 0.915 and mDice 0.899, which violates the mathematical relation IoU <= Dice for any binary segmentation, and Polyp-PVT is listed on CVC-ClinicDB with mDice 0.937, which exceeds DPE-Net's 0.931 despite the text claiming DPE-Net 'leads the pack' with the highest mDice. These are correctness and reproducibility concerns about the reported baselines, not circularity: they do not show that the proposed model's result reduces by construction to its own inputs. Accordingly, the circularity score is low.

Assumptions & free parameters 6 free parameters · 3 assumptions · 0 invented entities

The central claim loads on a set of hyperparameters and dataset assumptions, but these are ordinary experimental choices, not new theoretical entities. The paper adds no new particles, forces, or conserved quantities. Its contribution is an architecture configuration plus benchmark numbers.

free parameters (6)
  • learning rate = 1e-4 for DPE-Net, 1e-3 for ablation Network 3
    Chosen by hand, not derived; the ablation shows performance shifts with the learning rate.
  • mini-batch size = 8
    Chosen for training on the available GPU; no sensitivity analysis is reported.
  • momentum = 0.9
    Standard SGDM setting, not optimized per dataset.
  • training epochs = 40
    Fixed epoch count, not selected by validation convergence.
  • input resolution = 384x288
    Resized to match ClinicDB dimensions; affects all comparisons but is not varied.
  • encoder depth and channel widths = not reported
    The architecture figures show stages but exact channel counts and stage count are absent; these choices determine the 3.4 million parameter count and the accuracy.
assumptions (3)
  • domain assumption Ground-truth polyp masks in Kvasir and CVC-ClinicDB are accurate and consistent.
    The paper uses these public labels as target outputs without auditing label quality; mislabeled masks would bias all reported metrics.
  • domain assumption The 80/10/10 split yields test sets comparable to those used for quoted baseline numbers.
    Table II compares DPE-Net with published numbers; if baseline numbers come from different splits or protocols, the comparison is invalid.
  • standard math Standard CNN operations (convolution, ReLU, batch norm, max pooling, transpose convolution) behave as implemented in MATLAB.
    The paper relies on these primitives without formal specification.

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Cite this review

Pith. "Pith review of DPE-Net: Dual-Parallel Encoder Based Network for Semantic Segmentation of Polyps." pith.science (2026). https://pith.science/paper/IBM6BWKT

@misc{pith2026241200888,
  author       = {Pith},
  title        = {Pith review of: DPE-Net: Dual-Parallel Encoder Based Network for Semantic Segmentation of Polyps},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/IBM6BWKT}},
  note         = {Machine review of arXiv:2412.00888}
}
read the original abstract

In medical imaging, efficient segmentation of colon polyps plays a pivotal role in minimally invasive solutions for colorectal cancer. This study introduces a novel approach employing two parallel encoder branches within a network for polyp segmentation. One branch of the encoder incorporates the dual convolution blocks that have the capability to maintain feature information over increased depths, and the other block embraces the single convolution block with the addition of the previous layer's feature, offering diversity in feature extraction within the encoder, combining them before transpose layers with a depth-wise concatenation operation. Our model demonstrated superior performance, surpassing several established deep-learning architectures on the Kvasir and CVC-ClinicDB datasets, achieved a Dice score of 0.919, a mIoU of 0.866 for the Kvasir dataset, and a Dice score of 0.931 and a mIoU of 0.891 for the CVC-ClinicDB. The visual and quantitative results highlight the efficacy of our model, potentially setting a new model in medical image segmentation.

Figures

Figures reproduced from arXiv: 2412.00888 by the authors.

Figure 1
Figure 1. The detailed architecture diagram based on a dual encoder-based Network for polyp's segmentation. [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. The single and dual convolutions block repeatedly used in the DPE network [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗
Figure 4
Figure 4. visually compares the segmentation results on the [PITH_FULL_IMAGE:figures/full_fig_p003_4.png] view at source ↗

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Reference graph

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