Pith. sign in

REVIEW 3 major objections 2 minor 42 references

LightVesselNet: An Ultra-Lightweight Sub-100K Parameter Network for Retinal Blood Vessel Segmentation

T0 review · 3 major / 2 minor · reviewed 2026-06-28 · grok-4.3

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read A network with only 75,000 parameters segments retinal blood vessels competitively with much larger models.

desk verdict LightVesselNet combines standard lightweight modules into a 75K-param encoder-decoder and reports competitive vessel segmentation numbers on five datasets, but the abstract supplies no ablations or training details to show the modules are what deliver the result. read the letter →

arxiv 2606.05354 v1 pith:PGUWSZFS submitted 2026-06-03 cs.CV

Shadman Sobhan , Farhana Jalil This is my paper · ORCID
classification cs.CV
keywords retinalvesselsegmentationlightweightneuralnetworkencoder-decoderarchitectureattentionmechanismsmedicalimageanalysisedgedeploymentdiabeticretinopathydetection
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

Retinal blood vessel segmentation supports early detection of diabetic retinopathy and glaucoma. Most accurate deep learning models for this task demand heavy computation that prevents use on edge devices. LightVesselNet demonstrates that a compact encoder-decoder can reach similar sensitivity and Dice scores on five standard datasets by combining channel-spatial attention, a multi-scale bottleneck module, subpixel upsampling, and an edge residual connection. The design yields improved performance relative to parameter count or GFLOPs and maintains accuracy across datasets without extra tuning. This makes the network practical for mobile screening tools and low-resource clinics.

What carries the argument

LightVesselNet, a compact encoder-decoder architecture that uses channel-spatial attention, multi-scale bottleneck aggregation, subpixel upsampling, and edge residual connections to retain vessel detail at low parameter count.

What would settle it

A sixth independent retinal dataset on which LightVesselNet records sensitivity or Dice scores substantially below those of larger competing models.

Watch

Extended reading notes

Core claim

LightVesselNet is a compact encoder-decoder network containing only 75K parameters that incorporates channel and spatial attention mechanisms, a multi-scale feature aggregation module at the bottleneck, subpixel upsampling in the decoder, and a dedicated edge residual connection to preserve fine vessel detail. On the DRIVE, STARE, CHASEDB1, FIVES, and HRF datasets it records sensitivity scores of 0.8189, 0.8499, 0.8640, 0.8634, 0.8096 and Dice coefficients of 0.8070, 0.8072, 0.8181, 0.8649, 0.7686 while showing better efficiency than state-of-the-art models and confirmed cross-dataset generalization.

Load-bearing premise

The specific combination of channel-spatial attention, multi-scale bottleneck, subpixel upsampling, and edge residual connection is enough to preserve fine vessel detail and generalization across datasets at a 75K parameter count.

Editorial extensions

If this is right

  • The network can be deployed on edge devices for real-time retinal screening in resource-constrained settings.
  • It achieves higher performance per parameter and per GFLOP than state-of-the-art models on the tested datasets.
  • Cross-dataset evaluation shows the architecture generalizes without dataset-specific retraining.
  • LightVesselNet qualifies as a candidate for mobile clinical screening tools.

Reading between the lines

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

  • The same lightweight design pattern could be tested on other medical segmentation tasks that currently require large models.
  • Integration with smartphone-based fundus cameras might extend early eye-disease screening to remote areas.
  • Further compression or quantization experiments could check whether the model still meets accuracy targets on microcontrollers.
Share X Bluesky LinkedIn Reddit HN

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 LightVesselNet, a compact encoder-decoder network with approximately 75K parameters for retinal blood vessel segmentation. It incorporates channel-spatial attention, a multi-scale feature aggregation module at the bottleneck, subpixel upsampling in the decoder, and an edge residual connection. The work reports sensitivity and Dice scores on DRIVE, STARE, CHASEDB1, FIVES, and HRF, claims competitiveness with larger models, improved efficiency (performance vs. parameters/GFLOPs), and generalization via cross-dataset evaluation, positioning the model for low-resource clinical deployment.

Significance. If the performance and efficiency claims hold under standard training conditions, the result would be significant for enabling real-time retinal vessel segmentation on edge devices and mobile screening tools in ophthalmology, where computational constraints limit adoption of heavier models. The cross-dataset evaluation provides a positive element supporting generalization claims.

major comments (3)
  1. [Experimental Evaluation] Experimental Evaluation: The reported sensitivity (0.8189 on DRIVE, etc.) and Dice scores are presented without any ablation studies isolating the contributions of channel-spatial attention, multi-scale bottleneck, subpixel upsampling, or edge residual connection. This directly undermines verification of the central claim that this specific combination suffices for competitive performance at the 75K parameter budget.
  2. [Methods] Training Protocol: No details are provided on the loss function, optimizer, data augmentations, batch size, or hyperparameter selection procedure. Without this information, it cannot be confirmed that results were obtained using only standard, deployment-available practices rather than dataset-specific tuning.
  3. [Results] Results and Comparisons: Efficiency claims (Performance vs Parameter or GFlops) and competitiveness with SOTA models are asserted but lack tabulated parameter counts, GFLOPs, or statistical tests (e.g., error bars or significance) for the compared models, rendering the efficiency advantage unverifiable.
minor comments (2)
  1. [Abstract] Abstract: The list of five sensitivity and five Dice values is clear, but the manuscript should explicitly map each metric to its dataset in a table for immediate readability.
  2. [Proposed Method] Architecture description: The exact parameter breakdown per module (attention, bottleneck, etc.) should be stated numerically to support the sub-100K total.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive and detailed comments. We address each major point below and commit to revisions that will strengthen the manuscript without misrepresenting the original work.

read point-by-point responses

  1. Referee: [Experimental Evaluation] The reported sensitivity (0.8189 on DRIVE, etc.) and Dice scores are presented without any ablation studies isolating the contributions of channel-spatial attention, multi-scale bottleneck, subpixel upsampling, or edge residual connection. This directly undermines verification of the central claim that this specific combination suffices for competitive performance at the 75K parameter budget.

    Authors: We agree that ablation studies would provide stronger evidence for the contribution of each architectural element. In the revised manuscript we will add ablation experiments (on DRIVE) that systematically remove or replace channel-spatial attention, the multi-scale aggregation module, subpixel upsampling, and the edge residual connection, reporting the resulting changes in sensitivity and Dice score. revision: yes

  2. Referee: [Methods] Training Protocol: No details are provided on the loss function, optimizer, data augmentations, batch size, or hyperparameter selection procedure. Without this information, it cannot be confirmed that results were obtained using only standard, deployment-available practices rather than dataset-specific tuning.

    Authors: We acknowledge the omission. The revised manuscript will contain a dedicated training-protocol subsection that specifies the loss (Dice + binary cross-entropy), optimizer (Adam, lr = 1e-3), augmentations (random rotation, horizontal/vertical flip, brightness jitter), batch size (8), number of epochs, and the hyperparameter search procedure employed. revision: yes

  3. Referee: [Results] Results and Comparisons: Efficiency claims (Performance vs Parameter or GFlops) and competitiveness with SOTA models are asserted but lack tabulated parameter counts, GFLOPs, or statistical tests (e.g., error bars or significance) for the compared models, rendering the efficiency advantage unverifiable.

    Authors: We will add a comparison table listing parameter counts and GFLOPs for LightVesselNet and all referenced models. For statistical reliability we will report mean and standard deviation over three independent training runs with different random seeds; if additional compute is required we will note this limitation explicitly. revision: partial

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity; empirical results reported directly from experiments.

full rationale

The paper proposes an architecture (channel-spatial attention, multi-scale bottleneck, subpixel upsampling, edge residual) and reports sensitivity/Dice metrics as direct experimental outcomes on five public datasets. No equations, fitted parameters renamed as predictions, self-citation chains, or uniqueness theorems appear in the provided text. Performance claims rest on standard training and evaluation rather than any derivation that reduces to inputs by construction.

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

Only abstract available; no explicit free parameters, axioms or invented entities are stated. The design implicitly assumes that the listed architectural modules suffice for accuracy at 75K parameters.

how reviews work

0 comments
Cite this review

Pith. "Pith review of LightVesselNet: An Ultra-Lightweight Sub-100K Parameter Network for Retinal Blood Vessel Segmentation." pith.science (2026). https://pith.science/paper/PGUWSZFS

@misc{pith2026260605354,
  author       = {Pith},
  title        = {Pith review of: LightVesselNet: An Ultra-Lightweight Sub-100K Parameter Network for Retinal Blood Vessel Segmentation},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/PGUWSZFS}},
  note         = {Machine review of arXiv:2606.05354}
}
read the original abstract

Retinal blood vessel segmentation plays a vital role in the early detection of diabetic retinopathy and glaucoma. While recent deep learning models have achieved great segmentation accuracy, they typically require heavy computational resources, making real-world deployment on edge devices difficult. In this paper, we propose LightVesselNet, an efficient neural network designed for retinal vessel segmentation in a resource-constrained environment. Despite containing only 75K parameters, LightVesselNet performs competitively with much larger models. The network employs a compact encoder decoder architecture enhanced with channel and spatial attention mechanisms, a multi-scale feature aggregation module at the bottleneck, and a subpixel upsampling strategy in the decoder. A dedicated edge residual connection preserves fine vessel detail throughout decoding. Extensive experiments on five publicly available datasets: DRIVE, STARE, CHASEDB1, FIVES, and HRF, yield sensitivity scores of 0.8189, 0.8499, 0.8640, 0.8634, 0.8096, and Dice coefficients of 0.8070, 0.8072, 0.8181, 0.8649, and 0.7686, respectively. LightVesselNet shows improved efficiency (Performance vs Parameter or GFlops) compared to State-of-the-Art models. Cross-dataset evaluation confirms the model's generalisation capability. Overall, LightVesselNet is a strong candidate for deployment in low-resource clinical settings and mobile screening tools.

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

42 extracted references · 5 canonical work pages

  1. [1]

    Garvin, and Milan Sonka

    Abr `amoff, Michael D., Mona K. Garvin, and Milan Sonka. ”Retinal imaging and image analysis.” IEEE reviews in biomedical engineer- ing 3 (2010): 169-208

    2010

  2. [2]

    ”Fives: A fundus image dataset for artificial intelli- gence based vessel segmentation.” Scientific data 9.1 (2022): 475

    Jin, Kai, et al. ”Fives: A fundus image dataset for artificial intelli- gence based vessel segmentation.” Scientific data 9.1 (2022): 475

    2022

  3. [3]

    ”A review of retinal vessel seg- mentation for fundus image analysis.” Engineering Applications of Artificial Intelligence 128 (2024): 107454

    Qin, Qing, and Yuanyuan Chen. ”A review of retinal vessel seg- mentation for fundus image analysis.” Engineering Applications of Artificial Intelligence 128 (2024): 107454

    2024

  4. [4]

    ”Global causes of blindness and distance vi- sion impairment 1990–2020: a systematic review and meta-analysis.” The Lancet Global Health 5.12 (2017): e1221-e1234

    Flaxman, Seth R., et al. ”Global causes of blindness and distance vi- sion impairment 1990–2020: a systematic review and meta-analysis.” The Lancet Global Health 5.12 (2017): e1221-e1234

    1990

  5. [5]

    Blindness and vi- sion impairment,

    World Health Organization, “Blindness and vi- sion impairment,” Available:https://www. who.int/news-room/fact-sheets/detail/ blindness-and-visual-impairment. Accessed: May 24, 2026

    2026

  6. [6]

    ”TNF-αis an independent serum marker for proliferative retinopathy in type 1 diabetic patients.” Journal of Diabetes and its Complications 22.5 (2008): 309-316

    Gustavsson, Carin, et al. ”TNF-αis an independent serum marker for proliferative retinopathy in type 1 diabetic patients.” Journal of Diabetes and its Complications 22.5 (2008): 309-316

    2008

  7. [7]

    ”Enhanced retinal blood vessels segmenta- tion using deep learning and residual network.” Intelligence-Based Medicine 12 (2025): 100263

    Lamrani, Driss, et al. ”Enhanced retinal blood vessels segmenta- tion using deep learning and residual network.” Intelligence-Based Medicine 12 (2025): 100263

    2025

  8. [8]

    ”Detection of blood vessels in retinal images using two-dimensional matched filters.” IEEE Transactions on medical imaging 8.3 (1989): 263-269

    Chaudhuri, Subhasis, et al. ”Detection of blood vessels in retinal images using two-dimensional matched filters.” IEEE Transactions on medical imaging 8.3 (1989): 263-269

    1989

Show all 42 references
  1. [9]

    Memari, Nogol, et al. ”Retinal blood vessel segmentation by using matched filtering and fuzzy c-means clustering with integrated level set method for diabetic retinopathy assessment.” Journal of Medical and Biological Engineering 39.5 (2019): 713-731

    2019

  2. [10]

    Ouyang, Yihao, et al. ”A novel hybrid approach for retinal vessel segmentation with dynamic long-range dependency and multi-scale retinal edge fusion enhancement.” Pattern Analysis and Applications 28.4 (2025): 156

    2025

  3. [11]

    ”State-of-the-art retinal vessel segmentation with minimalistic models.” Scientific Reports 12.1 (2022): 6174

    Galdran, Adrian, et al. ”State-of-the-art retinal vessel segmentation with minimalistic models.” Scientific Reports 12.1 (2022): 6174

    2022

  4. [12]

    ”A survey on deep learning in medical image analysis.” Medical image analysis 42 (2017): 60-88

    Litjens, Geert, et al. ”A survey on deep learning in medical image analysis.” Medical image analysis 42 (2017): 60-88

    2017

  5. [13]

    ”U-net: Convolutional networks for biomedical image segmentation.” Inter- national Conference on Medical image computing and computer- assisted intervention

    Ronneberger, Olaf, Philipp Fischer, and Thomas Brox. ”U-net: Convolutional networks for biomedical image segmentation.” Inter- national Conference on Medical image computing and computer- assisted intervention. Cham: Springer international publishing, 2015

    2015

  6. [14]

    ”An image is worth 16x16 words: Transformers for image recognition at scale.” arXiv preprint arXiv:2010.11929 (2020)

    Dosovitskiy, Alexey, et al. ”An image is worth 16x16 words: Transformers for image recognition at scale.” arXiv preprint arXiv:2010.11929 (2020)

    2010 arXiv

  7. [15]

    ”Transunet: Transformers make strong encoders for medical image segmentation.” arXiv preprint arXiv:2102.04306 (2021)

    Chen, Jieneng, et al. ”Transunet: Transformers make strong encoders for medical image segmentation.” arXiv preprint arXiv:2102.04306 (2021)

    2021 arXiv

  8. [16]

    ”Prune-Quantize-Distill: An Or- dered Pipeline for Efficient Neural Network Compression.” arXiv preprint arXiv:2604.04988 (2026)

    Zhou, Longsheng, and Yu Shen. ”Prune-Quantize-Distill: An Or- dered Pipeline for Efficient Neural Network Compression.” arXiv preprint arXiv:2604.04988 (2026)

    2026 arXiv

  9. [17]

    D., Valentina Kouznetsova, and Michael Goldbaum

    Hoover, A. D., Valentina Kouznetsova, and Michael Goldbaum. ”Locating blood vessels in retinal images by piecewise threshold SHADMANet al.: LIGHTVESSELNET 13 probing of a matched filter response.” IEEE Transactions on Medical imaging 19.3 (2000): 203-210

    2000

  10. [18]

    ”Ridge-based vessel segmentation in color images of the retina.” IEEE transactions on medical imaging 23.4 (2004): 501-509

    Staal, Joes, et al. ”Ridge-based vessel segmentation in color images of the retina.” IEEE transactions on medical imaging 23.4 (2004): 501-509

    2004

  11. [19]

    ”An ensemble classification- based approach applied to retinal blood vessel segmentation.” IEEE transactions on biomedical engineering 59.9 (2012): 2538-2548

    Fraz, Muhammad Moazam, et al. ”An ensemble classification- based approach applied to retinal blood vessel segmentation.” IEEE transactions on biomedical engineering 59.9 (2012): 2538-2548

    2012

  12. [20]

    ”Retinalitenet: A lightweight transformer based cnn for retinal feature segmentation.” Proceedings of the IEEE/CVF conference on computer vision and pattern recognition

    Mehmood, Mehwish, et al. ”Retinalitenet: A lightweight transformer based cnn for retinal feature segmentation.” Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2024

    2024

  13. [21]

    ”LVS-Net: A Lightweight Vessels Seg- mentation Network for Retinal Image Analysis.” arXiv preprint arXiv:2412.05968 (2024)

    Mehmood, Mehwish, et al. ”LVS-Net: A Lightweight Vessels Seg- mentation Network for Retinal Image Analysis.” arXiv preprint arXiv:2412.05968 (2024)

    2024

  14. [22]

    ”LFA- Net: A Lightweight Network with LiteFusion Attention for Retinal Vessel Segmentation.” 2025 6th International Conference on Com- puter Vision and Data Mining (ICCVDM)

    Mehmood, Mehwish, Ivor Spence, and Muhammad Fahim. ”LFA- Net: A Lightweight Network with LiteFusion Attention for Retinal Vessel Segmentation.” 2025 6th International Conference on Com- puter Vision and Data Mining (ICCVDM). IEEE, 2025

    2025

  15. [23]

    Mehmood, Mehwish, et al. ”LFRA-Net: A Lightweight Focal and Region-Aware Attention Network for Retinal Vessel Segmentation.” 2025 International Conference on Digital Image Computing: Tech- niques and Applications (DICTA). IEEE, 2025

    2025

  16. [24]

    ”Retinal vessel seg- mentation based on a lightweight U-Net and reverse attention.” Mathematics 13.13 (2025): 2203

    Hernandez-Gutierrez, Fernando Daniel, et al. ”Retinal vessel seg- mentation based on a lightweight U-Net and reverse attention.” Mathematics 13.13 (2025): 2203

    2025

  17. [25]

    ”Robust vessel segmentation in fundus images.” International journal of biomedical imaging 2013.1 (2013): 154860

    Budai, Attila, et al. ”Robust vessel segmentation in fundus images.” International journal of biomedical imaging 2013.1 (2013): 154860

    2013

  18. [26]

    ”Unet++: A nested u-net architecture for med- ical image segmentation.” International workshop on deep learning in medical image analysis

    Zhou, Zongwei, et al. ”Unet++: A nested u-net architecture for med- ical image segmentation.” International workshop on deep learning in medical image analysis. Cham: Springer International Publishing, 2018

    2018

  19. [27]

    Indumathi

    Sathananthavathi, Vallikutti, and G. Indumathi. ”Encoder enhanced atrous (EEA) unet architecture for retinal blood vessel segmentation.” Cognitive Systems Research 67 (2021): 84-95

    2021

  20. [28]

    ”Wave-Net: A lightweight deep network for retinal vessel segmentation from fundus images.” Computers in biology and medicine 152 (2023): 106341

    Liu, Yanhong, et al. ”Wave-Net: A lightweight deep network for retinal vessel segmentation from fundus images.” Computers in biology and medicine 152 (2023): 106341

    2023

  21. [29]

    ”ResDO-UNet: A deep residual network for ac- curate retinal vessel segmentation from fundus images.” Biomedical Signal Processing and Control 79 (2023): 104087

    Liu, Yanhong, et al. ”ResDO-UNet: A deep residual network for ac- curate retinal vessel segmentation from fundus images.” Biomedical Signal Processing and Control 79 (2023): 104087

    2023

  22. [30]

    ”S-unet: A bridge-style u-net framework with a saliency mechanism for retinal vessel segmentation.” IEEE Access 7 (2019): 174167-174177

    Hu, Jingfei, et al. ”S-unet: A bridge-style u-net framework with a saliency mechanism for retinal vessel segmentation.” IEEE Access 7 (2019): 174167-174177

    2019

  23. [31]

    ”Squeeze-and-excitation networks.” Proceedings of the IEEE conference on computer vision and pattern recognition

    Hu, Jie, Li Shen, and Gang Sun. ”Squeeze-and-excitation networks.” Proceedings of the IEEE conference on computer vision and pattern recognition. 2018

    2018

  24. [32]

    ”Cbam: Convolutional block attention mod- ule.” Proceedings of the European conference on computer vision (ECCV)

    Woo, Sanghyun, et al. ”Cbam: Convolutional block attention mod- ule.” Proceedings of the European conference on computer vision (ECCV). 2018

    2018

  25. [33]

    ”Accurate retinal vessel segmentation in color fundus images via fully attention-based networks.” IEEE Journal of Biomedical and Health Informatics 25.6 (2020): 2071-2081

    Li, Kaiqi, et al. ”Accurate retinal vessel segmentation in color fundus images via fully attention-based networks.” IEEE Journal of Biomedical and Health Informatics 25.6 (2020): 2071-2081

    2020

  26. [34]

    ”MAGF-Net: A multiscale attention-guided fusion network for retinal vessel segmentation.” Measurement 206 (2023): 112316

    Li, Jianyong, et al. ”MAGF-Net: A multiscale attention-guided fusion network for retinal vessel segmentation.” Measurement 206 (2023): 112316

    2023

  27. [35]

    ”Focal loss for dense object detection.” Pro- ceedings of the IEEE international conference on computer vision

    Lin, Tsung-Yi, et al. ”Focal loss for dense object detection.” Pro- ceedings of the IEEE international conference on computer vision. 2017

    2017

  28. [36]

    ”M3U-CDV AE: Lightweight retinal vessel segmentation and refinement network.” Biomedical Signal Processing and Control 79 (2023): 104113

    Yu, Yang, and Hongqing Zhu. ”M3U-CDV AE: Lightweight retinal vessel segmentation and refinement network.” Biomedical Signal Processing and Control 79 (2023): 104113

    2023

  29. [37]

    ”G-net light: A lightweight modified google net for retinal vessel segmentation.” Photonics

    Iqbal, Shahzaib, et al. ”G-net light: A lightweight modified google net for retinal vessel segmentation.” Photonics. V ol. 9. No. 12. MDPI, 2022

    2022

  30. [38]

    ”An efficient and light weight deep learning model for accurate retinal vessels segmentation.” IEEE Access 11 (2022): 23107-23118

    Aurangzeb, Khursheed, et al. ”An efficient and light weight deep learning model for accurate retinal vessels segmentation.” IEEE Access 11 (2022): 23107-23118

    2022

  31. [39]

    ”Lhu-vt: A lightweight hypercom- plex u-net with vessel thickness-guided dice loss for retinal vessel segmentation.” Computers in Biology and Medicine 185 (2025): 109470

    Ahmed, Waqar, and Panos Liatsis. ”Lhu-vt: A lightweight hypercom- plex u-net with vessel thickness-guided dice loss for retinal vessel segmentation.” Computers in Biology and Medicine 185 (2025): 109470

    2025

  32. [40]

    ”Towards generalizable retina vessel segmentation with deformable graph priors.” Advances in Neural Information Processing Systems 38 (2026): 112600-112629

    Liu, Ke, et al. ”Towards generalizable retina vessel segmentation with deformable graph priors.” Advances in Neural Information Processing Systems 38 (2026): 112600-112629

    2026

  33. [41]

    ”Advancing retinal vessel segmentation with diversified deep convolutional neural networks.” IEEE Access 12 (2024): 141280-141290

    Tani, Tanzina Akter, and Jelena Te ˇsi´c. ”Advancing retinal vessel segmentation with diversified deep convolutional neural networks.” IEEE Access 12 (2024): 141280-141290

    2024

  34. [42]

    Faria, Fatema Tuj Johora, et al. ”Explainable convolutional neural networks for retinal fundus classification and cutting-edge segmen- tation models for retinal blood vessels from fundus images.” arXiv preprint arXiv:2405.07338 (2024)

    2024

Pith tools

Reviewed June 28, 2026 · model on record in the stance chip above.