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

Observation 4afe90da-f624-42f9-bd4f-c5e71e6c72ec · outbound

This paper cites Garvin, and Milan Sonka.

LightVesselNet: An Ultra-Lightweight Sub-100K Parameter Network for Retinal Blood Vessel Segmentation Garvin, and Milan Sonka

Reference 1

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Observation 6a5563e6-56a9-45cc-ad71-83eee7ee7f30 · outbound

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

LightVesselNet: An Ultra-Lightweight Sub-100K Parameter Network for Retinal Blood Vessel Segmentation ”Fives: A fundus image dataset for artificial intelli- gence based vessel segmentation.” Scientific data 9.1 (2022): 475

Reference 2

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Observation b28e63a1-0a87-4119-b183-c45f0e02eeb7 · outbound

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

LightVesselNet: An Ultra-Lightweight Sub-100K Parameter Network for Retinal Blood Vessel Segmentation ”A review of retinal vessel seg- mentation for fundus image analysis.” Engineering Applications of Artificial Intelligence 128 (2024): 107454

Reference 3

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Observation 8902eeff-4aba-4208-bdb6-d502f8446e52 · outbound

This paper cites ”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.

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

Reference 4

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This paper cites Blindness and vi- sion impairment,.

LightVesselNet: An Ultra-Lightweight Sub-100K Parameter Network for Retinal Blood Vessel Segmentation Blindness and vi- sion impairment,

Reference 5

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Observation 1da90b58-cfd1-4ad8-a598-f4def849a304 · outbound

This paper cites ”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.

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

Reference 6

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Observation 8eb6b67c-67f5-40b0-b7b7-d2bd23e19442 · outbound

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

LightVesselNet: An Ultra-Lightweight Sub-100K Parameter Network for Retinal Blood Vessel Segmentation ”Enhanced retinal blood vessels segmenta- tion using deep learning and residual network.” Intelligence-Based Medicine 12 (2025): 100263

Reference 7

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Observation 3a34ca53-d833-4c37-a877-1393222315a2 · outbound

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

LightVesselNet: An Ultra-Lightweight Sub-100K Parameter Network for Retinal Blood Vessel Segmentation ”Detection of blood vessels in retinal images using two-dimensional matched filters.” IEEE Transactions on medical imaging 8.3 (1989): 263-269

Reference 8

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This paper cites an unresolved cited work.

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

Reference 9

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Observation 971df42d-37d8-4949-a2b9-f70498562f41 · outbound

This paper cites an unresolved cited work.

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

Reference 10

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Observation be257fb8-9575-4874-b3d8-16a30c18da75 · outbound

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

LightVesselNet: An Ultra-Lightweight Sub-100K Parameter Network for Retinal Blood Vessel Segmentation ”State-of-the-art retinal vessel segmentation with minimalistic models.” Scientific Reports 12.1 (2022): 6174

Reference 11

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Observation c7b08bef-302f-474c-8766-d4b9eadebf9e · outbound

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

LightVesselNet: An Ultra-Lightweight Sub-100K Parameter Network for Retinal Blood Vessel Segmentation ”A survey on deep learning in medical image analysis.” Medical image analysis 42 (2017): 60-88

Reference 12

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Observation dba61134-aa53-4808-8dcf-8d3254d27060 · outbound

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

LightVesselNet: An Ultra-Lightweight Sub-100K Parameter Network for Retinal Blood Vessel Segmentation ”U-net: Convolutional networks for biomedical image segmentation.” Inter- national Conference on Medical image computing and computer- assisted intervention

Reference 13

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Observation 543789c8-c6f0-4289-b20f-34df9d9d4fd4 · outbound

This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

LightVesselNet: An Ultra-Lightweight Sub-100K Parameter Network for Retinal Blood Vessel Segmentation An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

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Observation 5cc6ec04-aaa4-4f97-85a0-7e63f63a8b1a · outbound

This paper cites TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation.

LightVesselNet: An Ultra-Lightweight Sub-100K Parameter Network for Retinal Blood Vessel Segmentation TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation

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Observation b359224d-e401-487c-a4e3-6d5ee5c52af0 · outbound

This paper cites Prune-Quantize-Distill: An Ordered Pipeline for Efficient Neural Network Compression.

LightVesselNet: An Ultra-Lightweight Sub-100K Parameter Network for Retinal Blood Vessel Segmentation Prune-Quantize-Distill: An Ordered Pipeline for Efficient Neural Network Compression

Reference 16

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Observation 023e235b-9ee9-42bf-b394-f2ac4146b84a · outbound

This paper cites D., Valentina Kouznetsova, and Michael Goldbaum.

LightVesselNet: An Ultra-Lightweight Sub-100K Parameter Network for Retinal Blood Vessel Segmentation D., Valentina Kouznetsova, and Michael Goldbaum

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Observation 4caa4f30-ae03-438d-b25f-19027a544eb2 · outbound

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

LightVesselNet: An Ultra-Lightweight Sub-100K Parameter Network for Retinal Blood Vessel Segmentation ”Ridge-based vessel segmentation in color images of the retina.” IEEE transactions on medical imaging 23.4 (2004): 501-509

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Observation 9a8208f5-1b87-4c1e-ab99-bd634fd486be · outbound

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

LightVesselNet: An Ultra-Lightweight Sub-100K Parameter Network for Retinal Blood Vessel Segmentation ”An ensemble classification- based approach applied to retinal blood vessel segmentation.” IEEE transactions on biomedical engineering 59.9 (2012): 2538-2548

Reference 19

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Observation 0f7ce918-faae-4e31-bd5d-44b15d17e350 · outbound

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

LightVesselNet: An Ultra-Lightweight Sub-100K Parameter Network for Retinal Blood Vessel Segmentation ”Retinalitenet: A lightweight transformer based cnn for retinal feature segmentation.” Proceedings of the IEEE/CVF conference on computer vision and pattern recognition

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Observation 87cd7679-ffbf-49e4-aae1-feff17ef3586 · outbound

This paper cites LVS-Net: A Lightweight Vessels Segmentation Network for Retinal Image Analysis.

LightVesselNet: An Ultra-Lightweight Sub-100K Parameter Network for Retinal Blood Vessel Segmentation LVS-Net: A Lightweight Vessels Segmentation Network for Retinal Image Analysis

Reference 21

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Observation 27003759-80dd-4f0a-ba12-724c7d7d1d7b · outbound

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

LightVesselNet: An Ultra-Lightweight Sub-100K Parameter Network for Retinal Blood Vessel Segmentation ”LFA- Net: A Lightweight Network with LiteFusion Attention for Retinal Vessel Segmentation.” 2025 6th International Conference on Com- puter Vision and Data Mining (ICCVDM)

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Observation adc35e18-99f5-4e10-84c2-3bf359c4a5ea · outbound

This paper cites an unresolved cited work.

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

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Observation 55aa3417-d23b-416c-811e-e7320f272768 · outbound

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

LightVesselNet: An Ultra-Lightweight Sub-100K Parameter Network for Retinal Blood Vessel Segmentation ”Retinal vessel seg- mentation based on a lightweight U-Net and reverse attention.” Mathematics 13.13 (2025): 2203

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Observation d3ccc228-6ed4-4535-909c-0fe4310c6671 · outbound

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

LightVesselNet: An Ultra-Lightweight Sub-100K Parameter Network for Retinal Blood Vessel Segmentation ”Robust vessel segmentation in fundus images.” International journal of biomedical imaging 2013.1 (2013): 154860

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Observation 1b7c4022-436f-4fc9-8b30-3efb2fc28ab2 · outbound

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

LightVesselNet: An Ultra-Lightweight Sub-100K Parameter Network for Retinal Blood Vessel Segmentation ”Unet++: A nested u-net architecture for med- ical image segmentation.” International workshop on deep learning in medical image analysis

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Observation 6984059c-7457-4129-987a-24a5564ebcea · outbound

This paper cites Indumathi.

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

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

LightVesselNet: An Ultra-Lightweight Sub-100K Parameter Network for Retinal Blood Vessel Segmentation ”Wave-Net: A lightweight deep network for retinal vessel segmentation from fundus images.” Computers in biology and medicine 152 (2023): 106341

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Observation f3e4163d-0ff1-4758-9414-ccb7d62126a9 · outbound

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

LightVesselNet: An Ultra-Lightweight Sub-100K Parameter Network for Retinal Blood Vessel Segmentation ”ResDO-UNet: A deep residual network for ac- curate retinal vessel segmentation from fundus images.” Biomedical Signal Processing and Control 79 (2023): 104087

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Observation 3063b7c0-6d75-4a4e-b89c-07d5c4498bfb · outbound

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

LightVesselNet: An Ultra-Lightweight Sub-100K Parameter Network for Retinal Blood Vessel Segmentation ”S-unet: A bridge-style u-net framework with a saliency mechanism for retinal vessel segmentation.” IEEE Access 7 (2019): 174167-174177

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Observation 6d8318b9-60c9-46fd-9644-ed252ff462a5 · outbound

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

LightVesselNet: An Ultra-Lightweight Sub-100K Parameter Network for Retinal Blood Vessel Segmentation ”Squeeze-and-excitation networks.” Proceedings of the IEEE conference on computer vision and pattern recognition

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

LightVesselNet: An Ultra-Lightweight Sub-100K Parameter Network for Retinal Blood Vessel Segmentation ”Cbam: Convolutional block attention mod- ule.” Proceedings of the European conference on computer vision (ECCV)

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This paper cites ”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.

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

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

LightVesselNet: An Ultra-Lightweight Sub-100K Parameter Network for Retinal Blood Vessel Segmentation ”MAGF-Net: A multiscale attention-guided fusion network for retinal vessel segmentation.” Measurement 206 (2023): 112316

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

LightVesselNet: An Ultra-Lightweight Sub-100K Parameter Network for Retinal Blood Vessel Segmentation ”Focal loss for dense object detection.” Pro- ceedings of the IEEE international conference on computer vision

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Observation fefd9154-7ff8-40e3-a6a2-401b4bab9c26 · outbound

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

LightVesselNet: An Ultra-Lightweight Sub-100K Parameter Network for Retinal Blood Vessel Segmentation ”M3U-CDV AE: Lightweight retinal vessel segmentation and refinement network.” Biomedical Signal Processing and Control 79 (2023): 104113

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Observation 63513ef2-ae2b-4c07-8fa5-5a7bca829ee5 · outbound

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

LightVesselNet: An Ultra-Lightweight Sub-100K Parameter Network for Retinal Blood Vessel Segmentation ”G-net light: A lightweight modified google net for retinal vessel segmentation.” Photonics

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Observation 3c4beb33-3174-4bc3-95ff-93f4c46d085d · outbound

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

LightVesselNet: An Ultra-Lightweight Sub-100K Parameter Network for Retinal Blood Vessel Segmentation ”An efficient and light weight deep learning model for accurate retinal vessels segmentation.” IEEE Access 11 (2022): 23107-23118

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Observation d02678bb-b65f-4bc1-85cf-27df5f8d51ef · outbound

This paper cites ”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.

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

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

LightVesselNet: An Ultra-Lightweight Sub-100K Parameter Network for Retinal Blood Vessel Segmentation ”Towards generalizable retina vessel segmentation with deformable graph priors.” Advances in Neural Information Processing Systems 38 (2026): 112600-112629

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

LightVesselNet: An Ultra-Lightweight Sub-100K Parameter Network for Retinal Blood Vessel Segmentation ”Advancing retinal vessel segmentation with diversified deep convolutional neural networks.” IEEE Access 12 (2024): 141280-141290

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Observation de5283d4-8490-4384-b4b1-2d348d37abde · outbound

This paper cites Explainable Convolutional Neural Networks for Retinal Fundus Classification and Cutting-Edge Segmentation Models for Retinal Blood Vessels from Fundus Images.

LightVesselNet: An Ultra-Lightweight Sub-100K Parameter Network for Retinal Blood Vessel Segmentation Explainable Convolutional Neural Networks for Retinal Fundus Classification and Cutting-Edge Segmentation Models for Retinal Blood Vessels from Fundus Images

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