Paper Citation Record · LEDGER

Investigating the Visual Cues of CNNs for Vascular Segmentation: A Case Study in Microscopy and Fundus Imaging

As of 11 August 2026, this Paper Citation Record lists 35 of 35 outbound references and 0 inbound Pith citation observations for arXiv:2607.23371.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2607.23371 v1

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measured 35 of 35 reference resolution

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Pith citing papers itemized under the disclosed page cap.

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Source: cited_works

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35 of 35 outbound references displayed

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Outbound references

Observation fa4295bf-3196-4993-9ca5-df8769266878 · outbound

This paper cites A review of machine learning methods for reti- nal blood vessel segmentation and artery/vein classifica- tion,.

Investigating the Visual Cues of CNNs for Vascular Segmentation: A Case Study in Microscopy and Fundus Imaging A review of machine learning methods for reti- nal blood vessel segmentation and artery/vein classifica- tion,

Reference 1

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Observation 07b2ae0b-474f-48ef-a658-960d78e7a0ad · outbound

This paper cites All answers are in the images: A review of deep learning for cerebrovascular segmenta- tion,.

Investigating the Visual Cues of CNNs for Vascular Segmentation: A Case Study in Microscopy and Fundus Imaging All answers are in the images: A review of deep learning for cerebrovascular segmenta- tion,

Reference 2

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Observation bf33c7b3-28e6-4693-b7a6-208bb77244d7 · outbound

This paper cites Hu- man treelike tubular structure segmentation: A compre- hensive review and future perspectives,.

Investigating the Visual Cues of CNNs for Vascular Segmentation: A Case Study in Microscopy and Fundus Imaging Hu- man treelike tubular structure segmentation: A compre- hensive review and future perspectives,

Reference 3

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Observation bf8f0812-fb04-4306-98e8-f0e7155e2486 · outbound

This paper cites nnu-net: a self- configuring method for deep learning-based biomedi- cal image segmentation,.

Investigating the Visual Cues of CNNs for Vascular Segmentation: A Case Study in Microscopy and Fundus Imaging nnu-net: a self- configuring method for deep learning-based biomedi- cal image segmentation,

Reference 4

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Observation 79cd123d-b8ad-400b-a105-6cac690c521b · outbound

This paper cites nnu-net revisited: A call for rigorous valida- tion in 3d medical image segmentation,.

Investigating the Visual Cues of CNNs for Vascular Segmentation: A Case Study in Microscopy and Fundus Imaging nnu-net revisited: A call for rigorous valida- tion in 3d medical image segmentation,

Reference 5

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Observation 68c90af1-3e2a-4000-b419-8cd00e6382c4 · outbound

This paper cites Segment anything in medical images,.

Investigating the Visual Cues of CNNs for Vascular Segmentation: A Case Study in Microscopy and Fundus Imaging Segment anything in medical images,

Reference 6

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Observation 7d1b09c3-f3f4-4b19-8e99-2ec5f8837be9 · outbound

This paper cites Stop explaining black box machine learning models for high stakes decisions and use inter- pretable models instead,.

Investigating the Visual Cues of CNNs for Vascular Segmentation: A Case Study in Microscopy and Fundus Imaging Stop explaining black box machine learning models for high stakes decisions and use inter- pretable models instead,

Reference 7

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Observation ede7bb18-b440-4f3a-b2fb-6d9d1987286d · outbound

This paper cites Survey of explainable artificial intelligence techniques for biomedical imaging with deep neural net- works,.

Investigating the Visual Cues of CNNs for Vascular Segmentation: A Case Study in Microscopy and Fundus Imaging Survey of explainable artificial intelligence techniques for biomedical imaging with deep neural net- works,

Reference 8

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Observation 2f77c88a-6fc2-4f98-9612-a8c00b8480f5 · outbound

This paper cites This study was financed in part by the Coordena¸ c˜ ao de Aperfei¸ coamento de Pessoal de N ´ ıvel Superior - Brasil (CAPES) - Finance Code 001.

Investigating the Visual Cues of CNNs for Vascular Segmentation: A Case Study in Microscopy and Fundus Imaging This study was financed in part by the Coordena¸ c˜ ao de Aperfei¸ coamento de Pessoal de N ´ ıvel Superior - Brasil (CAPES) - Finance Code 001

Reference 9

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Observation a27469da-a38c-416f-a131-5ff24ea1e1db · outbound

This paper cites Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness,.

Investigating the Visual Cues of CNNs for Vascular Segmentation: A Case Study in Microscopy and Fundus Imaging Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness,

Reference 10

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Observation a2380567-e54c-4c3e-a0ad-f1384744c6f5 · outbound

This paper cites Understanding the effective receptive field in deep convolutional neural networks,.

Investigating the Visual Cues of CNNs for Vascular Segmentation: A Case Study in Microscopy and Fundus Imaging Understanding the effective receptive field in deep convolutional neural networks,

Reference 11

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Observation 053a90bb-44f7-417c-be1b-a0d9613fc7fb · outbound

This paper cites Survey of explainable ai techniques in healthcare,.

Investigating the Visual Cues of CNNs for Vascular Segmentation: A Case Study in Microscopy and Fundus Imaging Survey of explainable ai techniques in healthcare,

Reference 12

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Observation bc3af668-c12a-42ae-bee6-72dba20e219c · outbound

This paper cites Explainable deep learning models in medical image analysis,.

Investigating the Visual Cues of CNNs for Vascular Segmentation: A Case Study in Microscopy and Fundus Imaging Explainable deep learning models in medical image analysis,

Reference 13

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Observation b937a4c6-bfbd-4c75-87b1-6047521e9412 · outbound

This paper cites Rethinking the image feature biases ex- hibited by deep convolutional neural network models in image recognition,.

Investigating the Visual Cues of CNNs for Vascular Segmentation: A Case Study in Microscopy and Fundus Imaging Rethinking the image feature biases ex- hibited by deep convolutional neural network models in image recognition,

Reference 14

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Observation 3f68b9dc-5f1a-4681-bd59-d486133b4f31 · outbound

This paper cites Reducing texture bias of deep neural net- works via edge enhancing diffusion,.

Investigating the Visual Cues of CNNs for Vascular Segmentation: A Case Study in Microscopy and Fundus Imaging Reducing texture bias of deep neural net- works via edge enhancing diffusion,

Reference 15

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Observation 3f548599-9c12-49c8-b3b4-40dfd834aab1 · outbound

This paper cites Edges to shapes to concepts: Ad- versarial augmentation for robust vision,.

Investigating the Visual Cues of CNNs for Vascular Segmentation: A Case Study in Microscopy and Fundus Imaging Edges to shapes to concepts: Ad- versarial augmentation for robust vision,

Reference 16

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Observation 7b1b422b-e00a-46d5-bc4d-73989685955f · outbound

This paper cites Shape prior is not all you need: Discovering balance between texture and shape bias in cnn,.

Investigating the Visual Cues of CNNs for Vascular Segmentation: A Case Study in Microscopy and Fundus Imaging Shape prior is not all you need: Discovering balance between texture and shape bias in cnn,

Reference 17

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Observation b2de069f-af7f-4ebe-9864-0ecf7ab0f515 · outbound

This paper cites Shape or texture: Understanding discriminative features in cnns,.

Investigating the Visual Cues of CNNs for Vascular Segmentation: A Case Study in Microscopy and Fundus Imaging Shape or texture: Understanding discriminative features in cnns,

Reference 18

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Observation f68c1c09-9a52-450c-bdf4-97e71517e821 · outbound

This paper cites The origins and prevalence of texture bias in convolu- tional neural networks,.

Investigating the Visual Cues of CNNs for Vascular Segmentation: A Case Study in Microscopy and Fundus Imaging The origins and prevalence of texture bias in convolu- tional neural networks,

Reference 19

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Observation feb7a97a-8b2d-4311-a1b1-160767b7b875 · outbound

This paper cites The estima- tion of the gradient of a density function, with appli- cations in pattern recognition,.

Investigating the Visual Cues of CNNs for Vascular Segmentation: A Case Study in Microscopy and Fundus Imaging The estima- tion of the gradient of a density function, with appli- cations in pattern recognition,

Reference 20

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Observation 07c03417-7b82-4249-8260-041991fc3bb0 · outbound

This paper cites Shape Bias and Robustness Evaluation via Cue Decomposition for Image Classification and Segmentation.

Investigating the Visual Cues of CNNs for Vascular Segmentation: A Case Study in Microscopy and Fundus Imaging Shape Bias and Robustness Evaluation via Cue Decomposition for Image Classification and Segmentation

Reference 21

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Observation 57c49eb0-61c4-45d0-948a-1e3bca400cdd · outbound

This paper cites On the influence of shape, texture and color for learning seman- tic segmentation,.

Investigating the Visual Cues of CNNs for Vascular Segmentation: A Case Study in Microscopy and Fundus Imaging On the influence of shape, texture and color for learning seman- tic segmentation,

Reference 22

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This paper cites Holistically-nested edge detection,.

Investigating the Visual Cues of CNNs for Vascular Segmentation: A Case Study in Microscopy and Fundus Imaging Holistically-nested edge detection,

Reference 23

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This paper cites Convolutional neural networks rarely learn shape for semantic segmen- tation,.

Investigating the Visual Cues of CNNs for Vascular Segmentation: A Case Study in Microscopy and Fundus Imaging Convolutional neural networks rarely learn shape for semantic segmen- tation,

Reference 24

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Observation 808c4035-557a-4688-a434-238ef2adbf19 · outbound

This paper cites Receptive field size as a key design parameter for ultrasound image segmen- tation with u-net,.

Investigating the Visual Cues of CNNs for Vascular Segmentation: A Case Study in Microscopy and Fundus Imaging Receptive field size as a key design parameter for ultrasound image segmen- tation with u-net,

Reference 25

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This paper cites Understanding the influence of receptive field and network complexity in neural network-guided tem image analysis,.

Investigating the Visual Cues of CNNs for Vascular Segmentation: A Case Study in Microscopy and Fundus Imaging Understanding the influence of receptive field and network complexity in neural network-guided tem image analysis,

Reference 26

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Observation 2a0400ec-4066-413a-ba26-7390cddf027d · outbound

This paper cites Demystifying the effect of receptive field size in u-net models for medical image segmentation,.

Investigating the Visual Cues of CNNs for Vascular Segmentation: A Case Study in Microscopy and Fundus Imaging Demystifying the effect of receptive field size in u-net models for medical image segmentation,

Reference 27

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Observation 78f37133-836a-478a-8e85-13ffa9f749e8 · outbound

This paper cites A new dataset for measuring the performance of blood vessel segmentation methods under distribution shifts,.

Investigating the Visual Cues of CNNs for Vascular Segmentation: A Case Study in Microscopy and Fundus Imaging A new dataset for measuring the performance of blood vessel segmentation methods under distribution shifts,

Reference 28

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This paper cites Ridge-based vessel segmentation in color images of the retina,.

Investigating the Visual Cues of CNNs for Vascular Segmentation: A Case Study in Microscopy and Fundus Imaging Ridge-based vessel segmentation in color images of the retina,

Reference 29

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Observation f5b03b55-ea27-4979-8f4a-6ec76e055c80 · outbound

This paper cites Understanding deep learn- ing requires rethinking generalization,.

Investigating the Visual Cues of CNNs for Vascular Segmentation: A Case Study in Microscopy and Fundus Imaging Understanding deep learn- ing requires rethinking generalization,

Reference 30

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Investigating the Visual Cues of CNNs for Vascular Segmentation: A Case Study in Microscopy and Fundus Imaging Deep residual learning for image recognition,

Reference 31

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This paper cites Building skeleton models via 3-d medial surface axis thinning algorithms,.

Investigating the Visual Cues of CNNs for Vascular Segmentation: A Case Study in Microscopy and Fundus Imaging Building skeleton models via 3-d medial surface axis thinning algorithms,

Reference 32

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Observation 1137e1cd-6a22-4f29-b3d3-12b51ab20750 · outbound

This paper cites State-of-the-art retinal vessel segmentation with minimalistic models,.

Investigating the Visual Cues of CNNs for Vascular Segmentation: A Case Study in Microscopy and Fundus Imaging State-of-the-art retinal vessel segmentation with minimalistic models,

Reference 33

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Investigating the Visual Cues of CNNs for Vascular Segmentation: A Case Study in Microscopy and Fundus Imaging Very deep con- volutional networks for large-scale image recognition,

Reference 34

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Observation baa45b49-7deb-45b9-a498-0480ba8bf268 · outbound

This paper cites ves- selfm: A foundation model for universal 3d blood vessel segmentation,.

Investigating the Visual Cues of CNNs for Vascular Segmentation: A Case Study in Microscopy and Fundus Imaging ves- selfm: A foundation model for universal 3d blood vessel segmentation,

Reference 35

Resolution: unresolved; no resolver link, observed 2026-07-31T23:41:54.869614Z

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Pith citing papers

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