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REVIEW 6 major objections 7 minor 70 references

Multi-scale and Multi-path Cascaded Convolutional Network for Semantic Segmentation of Colorectal Polyps

T0 review · 6 major / 7 minor · reviewed 2026-08-11 · deepseek-v4-flash

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Pith's one-line read A compact CNN, MMCC-Net, is claimed to outperform eight state-of-the-art models on pixel-level colorectal polyp segmentation across six public datasets using about 1.43 million parameters.

desk verdict A sensible lightweight CNN with thorough experiments, but the claimed statistical superiority over strong baselines is not supported by the reported confidence intervals and split protocol. read the letter →

arxiv 2412.02443 v1 pith:Z4PXHIBV submitted 2024-12-03 eess.IV cs.CV

Malik Abdul Manan , Feng Jinchao , Muhammad Yaqub , Shahzad Ahmed , Syed Muhammad Ali Imran , Imran Shabir Chuhan , Haroon Ahmed Khan This is my paper · ORCID
classification eess.IVcs.CV
keywords colorectalpolypsegmentationsemanticmulti-scaleCNNcascadedconvolutionattentionmodulesfeatureenhancerdicelosscolonoscopy
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

This paper introduces MMCC-Net, a convolutional network for pixel-level segmentation of colorectal polyps in colonoscopy images. The authors claim that by routing features through multiple parallel paths at several scales and fusing them with dense skip connections, attention modules, and a feature enhancer, the network captures both fine edges and global context better than existing designs. On six public polyp datasets, they report Dice scores from 77.43 to 94.45 and mean IoU from 72.71 to 90.16, with point estimates above eight state-of-the-art baselines. The network uses about 1.43 million parameters, far fewer than most comparators, so the claim matters for clinical tools that need speed and low compute. If correct, accurate polyp localization could run cheaply and in near real time during colonoscopy.

What carries the argument

The load-bearing mechanism is the dense multi-scale feature aggregation written as $DFA = Fa \otimes F1 \otimes F2 \otimes F4$, $DFB = AFi \otimes E1 \otimes E2 \otimes E4 \otimes AFB$, and $DFC = DFB \otimes AFA$, where $\otimes$ is depth-wise concatenation. Three parallel routes produce features at different dilation and stride factors, a mid-block adds an attention-filtered path, and a feature enhancer preserves low-level spatial cues. This cascade lets the decoder combine local edges, small-polyp details, and broad context, while the joint loss $L_{seg} = L_{Dice} + L_{BCE}$ with L2 smoothing on the Dice term addresses class imbalance.

What would settle it

Run a paired per-image significance test, such as a Wilcoxon signed-rank or bootstrap test on per-image Dice, between MMCC-Net and FCB-SwinV2 and PVT-CASCADE on a CVC-ClinicDB split that keeps frames from the same colonoscopy video entirely in either training or testing; if the difference is not significant at p < 0.05, the paper's central outperformance claim fails.

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Extended reading notes

Core claim

The paper's central claim is that a carefully balanced convolutional network can outperform transformer-based and hybrid competitors on polyp segmentation without large parameter counts. MMCC-Net couples multi-scale and multi-path cascaded convolutions with three routes for feature fusion, two attention modules, and a feature enhancer, and it is trained with a joint Dice-plus-cross-entropy loss with L2 smoothing on the Dice term. Across Kvasir, CVC-ClinicDB, CVC-300, ETIS, CVC-ColonDB, and EndoCV2020, the authors report that MMCC-Net achieves the highest point estimates for Dice, MIoU, precision, recall, accuracy, and the lowest Hausdorff distance among the eight compared methods, with narrow confidence intervals. They interpret this as evidence that strong global context can come from cascaded convolutions and dense feature aggregation rather than from transformers.

Load-bearing premise

The paper's results stand on the assumption that the differences between MMCC-Net and the strongest baselines are real and not artifacts of random training variation, split choice, or data leakage between video frames; the reported confidence intervals overlap for some key metrics and no paired significance tests are shown.

Editorial extensions

If this is right

  • At roughly 1.43 million parameters and 20.85 G FLOPs, the model could run on modest hardware or edge devices in colonoscopy suites.
  • A joint Dice-plus-BCE loss with L2 smoothing can handle polyp/background imbalance without explicit class weighting, a recipe transferable to other lesion segmentation tasks.
  • Multi-scale dense fusion with few filters per layer may generalize to small or irregular polyps because low-level edge cues are preserved through the feature enhancer.
  • The same architecture could be adapted to video polyp segmentation or other lumen and tissue segmentation tasks that require boundary precision.
  • The low parameter count and short training time make repeated retraining on hospital-specific data practical.

Reading between the lines

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

  • Because the reported margins over the strongest baselines are often a few tenths of a percentage point and some confidence intervals overlap, a reader should treat the superiority claim as provisional until paired significance tests are reported.
  • A natural next check is to evaluate MMCC-Net on a CVC-ClinicDB split that keeps frames from the same colonoscopy video entirely in either training or testing, following the data-leakage caveat the paper itself cites for FCB-SwinV2.
  • The ablation pattern suggests the feature enhancer, rather than skip connections alone, drives much of the improvement; isolating that component on harder small-polyp datasets would be a direct stress test.
  • The combination of Grad-CAM heatmaps and a compact architecture points toward a screening assistant role, though real clinical deployment would require testing on diverse imaging equipment and lighting conditions.
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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

6 major / 7 minor

Summary. The manuscript proposes MMCC-Net, a lightweight CNN for colorectal polyp segmentation built from multi-scale multi-path cascaded convolutions, dense skip connections, two attention modules, and a feature enhancer, trained with a joint Dice and binary cross-entropy loss. Experiments are conducted on six public datasets (Kvasir, CVC-ClinicDB, CVC-300, ETIS, CVC-ColonDB, EndoCV2020) and compared with eight published models. The paper reports Dice scores from 77.43 to 94.45 and MIoU from 72.71 to 90.16 across datasets, approximately 1.43M parameters, repeated 10-run statistics, 5-fold cross-validation, ablations, loss/optimizer/LR sensitivity, HDD/AUC, and efficiency comparisons. The central claim is that MMCC-Net outperforms all eight SOTA models while being substantially more parameter-efficient.

Significance. The intended contribution is a parameter-efficient CNN that matches or beats transformer-based segmenters; such a model would have practical value for colonoscopy workflows. The paper's strengths are its breadth (six datasets, eight baselines), repeated-run design, ablation of architectural modules, and explicit discussion of failure cases and deployment issues. However, the statistical basis for the headline claim is not established: the largest advantages over the strongest baselines are tiny (0.02-0.13 Dice points), confidence intervals overlap, no paired significance tests are provided, and several reported intervals are arithmetically impossible. The additional risk of video-level leakage in the CVC-ClinicDB split, acknowledged in the paper's own discussion of FCB-SwinV2, could materially inflate the reported results. With corrected statistics and a leakage-free evaluation, the paper could be a solid empirical study, but the current evidence does not support the claimed superiority.

major comments (6)
  1. [Table 2] The claim that MMCC-Net "consistently outperforms" eight SOTA models is not supported by Table 2. On CVC-ClinicDB, MMCC-Net Dice is 94.45 ± 0.12 with 95% CI (94.19, 94.71) versus FCB-SwinV2 94.43 ± 0.13 (94.17, 94.69); the difference is 0.02 points and the intervals overlap almost entirely. On Kvasir, the corresponding difference is 0.13 Dice points with overlapping intervals. No paired significance test (Wilcoxon signed-rank, paired bootstrap, or corrected resampled t-test) is reported for any comparison, so the reported "superior performance" has no demonstrated statistical support. Please add paired tests over the 10 runs and report exact p-values or bootstrap CIs for each SOTA comparison.
  2. [Tables 2 and 4] The confidence intervals appear internally inconsistent. With n=10 runs and t_{0.025,9}=2.262, the Kvasir proposed Dice mean 92.65 and SD 0.13 imply a 95% CI of approximately (92.56, 92.74), not the reported (92.65, 93.25); the lower bound cannot equal the mean. Similar discrepancies appear in many rows (e.g., several CIs have both endpoints above the mean). Please recompute all CIs from the actual per-run results and state the critical value and formula used.
  3. [Section 4.1 / Table 1] The CVC-ClinicDB split is at risk of video-level data leakage. The dataset consists of 612 images from 29 colonoscopy videos, and Table 1 shows a 490/61/61 train/validation/test split, but the manuscript nowhere states that the split is video-aware. Section 2.2 itself notes that FCB-SwinV2 highlights video-sequence data leakage in CVC-ClinicDB. If frames from the same video appear in both training and test partitions, the reported ClinicDB numbers are inflated. Please either confirm that the split was performed at the video level and describe the procedure, or rerun Experiment 1 with a video-aware split.
  4. [Table 3] Table 3 (5-fold cross-validation) contradicts the text's claim that MMCC-Net "outperforms all other models regarding mDice, MIoU, precision, and recall across both datasets." On Kvasir, MMCC-Net's Dice is 92.29 ± 0.22, lower than PVT-CASCADE's 92.49 ± 0.31. This is a direct internal inconsistency in the central comparative claim; please correct the table or the text and discuss the discrepancy.
  5. [Section 3.2 / Eqs. (4)-(11)] The loss equations are not usable as written. Eq. (4) defines an L2 Dice loss, Eq. (8) defines Lseg = LDice + LBce, and Eq. (11) introduces α and γ in a placement that is dimensionally inconsistent with Eqs. (8)-(10); the grad-CAM text and Eq. (7) are also garbled. Since the joint loss and its hyperparameters (α=0.22, γ=1.9) are part of the method, please rewrite the loss derivation with consistent notation and verify every equation.
  6. [Section 4.3.1 / Table 8] The manuscript does not state whether α, γ, the learning rate, and other architectural choices were selected using the same test folds on which the final numbers are reported. If these hyperparameters were tuned on the CVC-ClinicDB and Kvasir test partitions, the reported confidence intervals are selection-conditional and would be optimistic. Please describe the model selection protocol used for each experiment.
minor comments (7)
  1. [Section 4.2] The text states that the study uses eight evaluation measures but then enumerates nine: Dice, accuracy, sensitivity, precision, specificity, IOU, AUC, CI, and HDD.
  2. [Figure 3] The architecture diagram is difficult to read at the resolution provided; please supply a higher-resolution version with all modules and pathways clearly labeled.
  3. [Eq. (17)] The Hausdorff distance formula is incorrect as printed: the second term should be max over b in B of min over a in A of ||b - a||, not another maximization over a in A.
  4. [Section 4.3.2] The citations "FCB-Former [27]" and "FCB-SwinV2 [32]" appear to be wrong; these should likely refer to references [47] and [52], respectively.
  5. [Tables 2, 5, and 6] Tables 5 and 6 report CVC-ClinicDB Dice of 94.41 and Kvasir Dice of 92.40, while Table 2 reports 94.45 and 92.65 for the same model; please clarify whether these are the same 10-run averages or different runs.
  6. [Section 4.3] The training-time description is inconsistent: the text says 80 epochs took about 8 hours with no improvement beyond 60 epochs (about 6 hours), while Table 10 reports a training time of 6.0 hours; please align these statements.
  7. [Equations (12)-(18)] Notation is inconsistent between "IOU" in Eq. (16) and "MIoU" in the tables; please unify the terminology and define HDD before first use in Section 4.2.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: MMCC-Net's performance claims are empirical comparisons against independently published baselines, with no fitted parameter renamed as a prediction.

full rationale

This paper is an empirical architecture study, not a derivation. The central claim that MMCC-Net outperforms eight SOTA models is supported by direct experimental measurements (Dice, MIoU, etc.) on public datasets, with all baselines run under the same protocol using open-source code. The loss function in Equations (8)-(11) defines an optimization objective rather than deriving the reported segmentation outputs from the definition of the metric; no predicted quantity is equal to an input by construction. Hyperparameters such as alpha = 0.22, gamma = 1.9, and learning rate 1e-4 were selected empirically, but this is standard practice and does not amount to fitting a parameter and then calling a closely related quantity a prediction. The paper's self-citations (e.g., references 23-27) appear in the literature review and methodology context but are not load-bearing for the superiority claim, which rests on the reported comparisons and ablations. The critique that the reported confidence intervals are internally inconsistent and that no paired significance tests are provided is a statistical-validity concern, not a circularity concern. Likewise, the possible video-level data leakage in CVC-ClinicDB is a data-protocol issue, not a case where the paper's conclusion is equivalent to its inputs. Therefore, no circular step can be identified under the required standard of quoting a specific reduction.

Assumptions & free parameters 4 free parameters · 4 assumptions · 1 invented entities

The model depends on tuned hyperparameters and hand-chosen scale factors. No new physical entities are introduced; the Feature Enhancer is an architectural module without independent falsifiable support outside the paper's own experiments.

free parameters (4)
  • alpha (loss weighting) = 0.22
    Weight balancing Dice and BCE terms in Eq. 11; chosen by experiments on validation data.
  • gamma (loss exponent) = 1.9
    Exponent in the BCE term of the combined loss; tuned on validation.
  • learning rate = 1e-4
    Selected from Table 8 as best on CVC-ClinicDB; affects all reported results.
  • dilation and stride factors for multi-scale branches = 1, 2, 4
    Hand-chosen scale factors in Eqs. 1 and 2; no sensitivity analysis is reported.
assumptions (4)
  • domain assumption Ground-truth annotations in the six datasets are correct and consistent.
    The paper uses these public annotations as the evaluation standard without auditing them.
  • domain assumption Training on Kvasir and CVC-ClinicDB and testing on four other datasets measures cross-dataset generalization.
    This is the standard evaluation protocol, but the paper does not justify that these four test sets are representative of clinical diversity.
  • standard math The 95% confidence intervals computed from 10 runs with a t-distribution are valid.
    The paper states sample size 10 and margin of error formula, but does not show the actual t-values used.
  • domain assumption The public benchmark datasets are representative of clinical polyp imaging.
    Section 5.1 acknowledges that public datasets may not capture clinical diversity, undermining this assumption.
invented entities (1)
  • Feature Enhancer (FE) module
    purpose: To preserve and enhance low-level spatial cues before later fusion.
    Introduced in Section 3.1.3; only internal ablation results are shown, with no external benchmark or independent variant comparison.

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Cite this review

Pith. "Pith review of Multi-scale and Multi-path Cascaded Convolutional Network for Semantic Segmentation of Colorectal Polyps." pith.science (2026). https://pith.science/paper/Z4PXHIBV

@misc{pith2026241202443,
  author       = {Pith},
  title        = {Pith review of: Multi-scale and Multi-path Cascaded Convolutional Network for Semantic Segmentation of Colorectal Polyps},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/Z4PXHIBV}},
  note         = {Machine review of arXiv:2412.02443}
}
read the original abstract

Colorectal polyps are structural abnormalities of the gastrointestinal tract that can potentially become cancerous in some cases. The study introduces a novel framework for colorectal polyp segmentation named the Multi-Scale and Multi-Path Cascaded Convolution Network (MMCC-Net), aimed at addressing the limitations of existing models, such as inadequate spatial dependence representation and the absence of multi-level feature integration during the decoding stage by integrating multi-scale and multi-path cascaded convolutional techniques and enhances feature aggregation through dual attention modules, skip connections, and a feature enhancer. MMCC-Net achieves superior performance in identifying polyp areas at the pixel level. The Proposed MMCC-Net was tested across six public datasets and compared against eight SOTA models to demonstrate its efficiency in polyp segmentation. The MMCC-Net's performance shows Dice scores with confidence intervals ranging between (77.08, 77.56) and (94.19, 94.71) and Mean Intersection over Union (MIoU) scores with confidence intervals ranging from (72.20, 73.00) to (89.69, 90.53) on the six databases. These results highlight the model's potential as a powerful tool for accurate and efficient polyp segmentation, contributing to early detection and prevention strategies in colorectal cancer.

Figures

Figures reproduced from arXiv: 2412.02443 by the authors.

Figure 1
Figure 1. The number of estimated deaths because of cancer in 2020 worldwide, including Colorectum cancer in males and females of all ages. Polyps are abnormal tissue growths that can appear on the body's surface, and the rectum, stomach, colon, and throat are places where they can be discovered [5]. Due to their potentially cancerous nature, accurate assessment is necessary in clinical settings, which includes analyzing chan… view at source ↗
Figure 2
Figure 2. The image sample with a ground truth mask from all six databases we used in our research for segmentation. The challenges in polyp segmentation are due to various factors, including the loss of local information, visual distractions due to polyp diversity, and the limitations of conventional segmentation algorithms that fail to provide a complete analysis. Deep learning (DL) for clinical diagnosis has revealed sever… view at source ↗
Figure 3
Figure 3. The architecture of the proposed MMCC-Net was tested on six publicly available datasets. In our algorithm, spatial dimensions are reduced due to average pooling during the forward pass of many convolutional architectures. Moreover, these operations are beneficial for capturing hierarchical features. However, the size of the output image should match the size of the original input image. Considering this, transposed … view at source ↗
Figures from the paper (10 more)
Figure 4
Figure 4. Figure 4: The dense features from multi-scale and multi-path by the proposed MMCC-Net [PITH_FULL_IMAGE:figures/full_fig_p011_4.png]
Figure 5
Figure 5. Figure 5: Grad-CAM heat map visualizations for MMCC-Net stages. Column 1 shows input images; the initial convolutional block heat maps are presented in Column 2; Columns 3-5 show results after the first, second, and third concatenation; Columns 6-7 present the results with dice …
Figure 6
Figure 6. Figure 6: The result of our proposed MMCC-Net and Seven SOTA Models on the Kvasir and CVC-ClinicDB datasets. Columns 1-2 show the original images and Ground Truth of the datasets; columns 3-9 show the segmented results of SOTA, and column 10 shows the proposed MMCC-net [PITH_FU…
Figure 7
Figure 7. Figure 7: illustrates the visual results of Experiment 2, highlighting that MMCC-Net has a notably lower rate of incorrect pixel predictions than other models. The analytical data for this experiment is provided in [PITH_FULL_IMAGE:figures/full_fig_p020_7.png]
Figure 8
Figure 8. Figure 8: The comparisons of our proposed MMCC-Net and Seven SOTA models on the CVC-300 and ETIS datasets. Columns 1-2 show the original images and ground truth of the datasets; columns 3-9 show the segmented results of SOTA, and column 10 shows the results of the proposed MMCC-…
Figure 9
Figure 9. Figure 9: The comparisons of our proposed MMCC-Net and Seven SOTA models on the ColonDB and EndoCV2020 datasets. Columns 1-2 show the original images and ground truth of the datasets; columns 3-9 show the segmented results of SOTA, and column 10 shows the results of the proposed…
Figure 10
Figure 10. Figure 10: The MMCC-Net architecture with the integration of additional modules to handle challenging polyp cases. 4.3.3 Ablation Study of MMCC-Net Experiments were conducted on the CVC-ClinicDB and Kvasir datasets to validate the performance of the MMCC-Net and the influence of…
Figure 11
Figure 11. Figure 11: Sample images with corresponding masks demonstrate superior performance in accurately segmenting polyp boundaries, highlighting its effectiveness in handling class imbalance. L2 loss on the Dice coefficient helps to provide a smooth gradient for optimization. This sta…
Figure 12
Figure 12. Figure 12: The training accuracy, validation accuracy, training loss, and validation loss of the proposed MMCC-Net with minimum and maximum values. MMCC-Net distinguishes itself by employing features across three levels, a significant advancement over other methods that typicall…
Figure 13
Figure 13. Figure 13: The failure examples of the proposed method are on Endocv2020 (left), Kvasir (middle), and CVC-ClinicDB (right) databases. Based on potential user feedback, we intend to improve the generalization characteristics of our network by testing it on colonoscopy images from…

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Reference graph

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Pith tools

Reviewed August 11, 2026 · model on record in the stance chip above.