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Paper Citation Record · LEDGER

Robustifying deep networks for image segmentation

As of 22 August 2026, this Paper Citation Record lists 26 of 26 outbound references and 0 inbound Pith citation observations for arXiv:1908.00656.

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

pith.paper-citation-record.v1
1908.00656 v1

Coverage vector

measured 26 of 26 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-14T15:44:43.820144Z

measured 26 of 26 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

26 of 26 outbound references displayed

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  • verified fuzzy6
  • unresolved19
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 0827c31f-1e41-427e-a8b7-302d24c9d9ce · outbound

This paper cites Machine Learning in Medical Imaging.

Robustifying deep networks for image segmentation Machine Learning in Medical Imaging

Reference 1

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Observation 83f2720b-8d72-41d3-8923-c8683d7117ab · outbound

This paper cites Machine Learning and Radiology.

Robustifying deep networks for image segmentation Machine Learning and Radiology

Reference 2

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Observation 15a66d3c-ce59-49cc-bce2-d694107c45f0 · outbound

This paper cites Machine learning approaches in medical image analysis: From detection to diagnosis.

Robustifying deep networks for image segmentation Machine learning approaches in medical image analysis: From detection to diagnosis

Reference 3

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This paper cites The Mythos of Model Interpretability.

Robustifying deep networks for image segmentation The Mythos of Model Interpretability

Reference 5

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Observation c27400d1-48ce-48a8-864e-8ef2108dc316 · outbound

This paper cites Methods for interpreting and understanding deep neural networks.

Robustifying deep networks for image segmentation Methods for interpreting and understanding deep neural networks

Reference 6

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Observation 321c75fc-67e9-4c4c-a232-4a120ba8c85a · outbound

This paper cites Explaining and Harnessing Adversarial Examples.

Robustifying deep networks for image segmentation Explaining and Harnessing Adversarial Examples

Reference 7

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Robustifying deep networks for image segmentation Unresolved cited work

Reference 8

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Observation 687451f3-6b64-41d3-9827-4136e6c5ff1d · outbound

This paper cites distilled.

Robustifying deep networks for image segmentation distilled

Reference 9

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Source-reported events for the cited work

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Observation 54ab843e-ac49-4f2c-b799-c89c48da1028 · outbound

This paper cites Distillation as a Defense to Adversarial Perturbations Against Deep Neural Networks,.

Robustifying deep networks for image segmentation Distillation as a Defense to Adversarial Perturbations Against Deep Neural Networks,

Reference 10

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Observation 1cb2c0b5-3959-4d38-bd72-39f25782ee32 · outbound

This paper cites 3D U-Net: learning dense volumetric segmentation from sparse annotation,.

Robustifying deep networks for image segmentation 3D U-Net: learning dense volumetric segmentation from sparse annotation,

Reference 11

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Robustifying deep networks for image segmentation Unresolved cited work

Reference 12

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Robustifying deep networks for image segmentation Unresolved cited work

Reference 13

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Robustifying deep networks for image segmentation Unresolved cited work

Reference 14

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Robustifying deep networks for image segmentation Unresolved cited work

Reference 15

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Robustifying deep networks for image segmentation Unresolved cited work

Reference 16

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Observation 78aed67b-2705-4aac-8a1e-bac0d12536bf · outbound

This paper cites The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS).

Robustifying deep networks for image segmentation The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS)

Reference 17

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Observation 0464e554-592e-4c33-ac90-2dc846e2cd89 · outbound

This paper cites Advancing The Cancer Genome Atlas glioma MRI collections with expert segmentation labels and radiomic features.

Robustifying deep networks for image segmentation Advancing The Cancer Genome Atlas glioma MRI collections with expert segmentation labels and radiomic features

Reference 18

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Robustifying deep networks for image segmentation Unresolved cited work

Reference 20

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This paper cites Towards Deep Learning Models Resistant to Adversarial Attacks.

Robustifying deep networks for image segmentation Towards Deep Learning Models Resistant to Adversarial Attacks

Reference 22

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Robustifying deep networks for image segmentation Efficient Defenses Against Adversarial Attacks

Reference 23

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Observation e4cada46-ee1c-4520-bc19-990113f7bb85 · outbound

This paper cites MagNet and "Efficient Defenses Against Adversarial Attacks" are Not Robust to Adversarial Examples.

Robustifying deep networks for image segmentation MagNet and "Efficient Defenses Against Adversarial Attacks" are Not Robust to Adversarial Examples

Reference 26

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Observation 1522c0bc-1a00-42a7-abd3-9d149ba8771b · outbound

This paper cites Exploring the Landscape of Spatial Robustness.

Robustifying deep networks for image segmentation Exploring the Landscape of Spatial Robustness

Reference 27

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This paper cites Tustison, B.B.

Robustifying deep networks for image segmentation Tustison, B.B

Reference 29

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Observation 9a8c5659-24aa-45eb-a73c-71922edf1027 · outbound

This paper cites Deep Residual Learning for Image Recognition.

Robustifying deep networks for image segmentation Deep Residual Learning for Image Recognition

Reference 2016

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Observation 650953c8-be3d-4a3a-8d7a-83eb244cc3da · outbound

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Robustifying deep networks for image segmentation Unresolved cited work

Reference 2017

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Observation eb3a4570-03b0-4ca5-93a0-f69a5e7f6aaf · outbound

This paper cites Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey.

Robustifying deep networks for image segmentation Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey

Reference 2018

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Pith citing papers

No inbound Pith citation observations are available.