Paper Citation Record · LEDGER

Modality-Independent Explainable Detection of Inaccurate Organ Segmentations Using Denoising Autoencoders

As of 21 August 2026, this Paper Citation Record lists 23 of 23 outbound references and 0 inbound Pith citation observations for arXiv:2504.12203.

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

pith.paper-citation-record.v1
2504.12203 v1

Coverage vector

measured 23 of 23 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-16T12:39:12.410631Z

measured 23 of 23 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+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

23 of 23 outbound references displayed

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verified fuzzy9
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Outbound references

Observation 24601b0a-b733-4805-be62-0761f5235665 · outbound

This paper cites This challenge is further complicated by automation bias, where clinicians tend to overly rely on auto-segmentations [1].

Modality-Independent Explainable Detection of Inaccurate Organ Segmentations Using Denoising Autoencoders This challenge is further complicated by automation bias, where clinicians tend to overly rely on auto-segmentations [1]

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:39:12.808831Z

Source-reported events for the cited work

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Observation 4433d2a6-d7bc-4345-83da-da774ee112ab · outbound

This paper cites MR Pelvis Segmentation Models To generate organ auto-segmentations for the MR pelvis use case, we utilized deep learning segmentation models described in Czipczer et al.

Modality-Independent Explainable Detection of Inaccurate Organ Segmentations Using Denoising Autoencoders MR Pelvis Segmentation Models To generate organ auto-segmentations for the MR pelvis use case, we utilized deep learning segmentation models described in Czipczer et al

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:39:12.795054Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 2e3ce17e-bc99-49d9-ac20-2725305f0621 · outbound

This paper cites MR Pelvis We evaluated the four methods on the auto -segmentations in the test set.

Modality-Independent Explainable Detection of Inaccurate Organ Segmentations Using Denoising Autoencoders MR Pelvis We evaluated the four methods on the auto -segmentations in the test set

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:39:12.781451Z

Source-reported events for the cited work

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Observation 229dbd5f-a415-4f9c-a621-1d6eff4fad84 · outbound

This paper cites We demonstrated that our method provides superior performance compared to existing solutions in the literature.

Modality-Independent Explainable Detection of Inaccurate Organ Segmentations Using Denoising Autoencoders We demonstrated that our method provides superior performance compared to existing solutions in the literature

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:39:12.766718Z

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation d33a3c25-9ba6-41ea-ba58-359ca1699806 · outbound

This paper cites Ethical statements can be found in Section 2.8 in Czipczer et al.

Modality-Independent Explainable Detection of Inaccurate Organ Segmentations Using Denoising Autoencoders Ethical statements can be found in Section 2.8 in Czipczer et al

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:39:12.752098Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 15f5c196-b628-4e16-917f-37ae75712c57 · outbound

This paper cites We thank the annotation team, including authors Zs.K., B.D.

Modality-Independent Explainable Detection of Inaccurate Organ Segmentations Using Denoising Autoencoders We thank the annotation team, including authors Zs.K., B.D

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:39:12.738040Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 981ade9f-8882-4fda-9711-f8de45840aa9 · outbound

This paper cites Automation bias: A systematic review of frequency, effect mediators, and mitigators,.

Modality-Independent Explainable Detection of Inaccurate Organ Segmentations Using Denoising Autoencoders Automation bias: A systematic review of frequency, effect mediators, and mitigators,

Reference 7

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Source-reported events for the cited work

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Observation 3d43c152-6d09-4ab5-ac42-805233248524 · outbound

This paper cites Quality assurance tool for organ at risk delineation in radiation therapy using a parametric statistical approach,.

Modality-Independent Explainable Detection of Inaccurate Organ Segmentations Using Denoising Autoencoders Quality assurance tool for organ at risk delineation in radiation therapy using a parametric statistical approach,

Reference 8

Resolution: verified exact; doi, observed 2026-08-16T12:39:12.529074Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation f8529c31-526c-4945-b22a-01b2df5ec671 · outbound

This paper cites Detecting When Pre- trained nnU-Net Models Fail Silently for Covid-19 Lung Lesion Segmentation,.

Modality-Independent Explainable Detection of Inaccurate Organ Segmentations Using Denoising Autoencoders Detecting When Pre- trained nnU-Net Models Fail Silently for Covid-19 Lung Lesion Segmentation,

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-16T12:39:12.355542Z

Source-reported events for the cited work

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Observation c1e5b21d-5e07-451f-8943-60c2d508b03c · outbound

This paper cites Dimensionality Reduction for Improving Out-of-Distribution Detection in Medical Image Segmentation,.

Modality-Independent Explainable Detection of Inaccurate Organ Segmentations Using Denoising Autoencoders Dimensionality Reduction for Improving Out-of-Distribution Detection in Medical Image Segmentation,

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-16T12:39:12.359383Z

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Observation 4d34dbf5-5f5c-4e77-9bb3-c86e1dc2cd0d · outbound

This paper cites A framework for automated contour quality assurance in radiation therapy including adaptive techniques,.

Modality-Independent Explainable Detection of Inaccurate Organ Segmentations Using Denoising Autoencoders A framework for automated contour quality assurance in radiation therapy including adaptive techniques,

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-16T12:39:12.363563Z

Source-reported events for the cited work

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Observation 5a1424a5-ba59-4f08-adb1-a30065640a67 · outbound

This paper cites Use of Variational Autoencoders with Unsupervised Learning to Detect Incorrect Organ Segmentations at CT,.

Modality-Independent Explainable Detection of Inaccurate Organ Segmentations Using Denoising Autoencoders Use of Variational Autoencoders with Unsupervised Learning to Detect Incorrect Organ Segmentations at CT,

Reference 12

Resolution: verified exact; doi, observed 2026-08-16T12:39:12.484562Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 46ca69e4-b4ab-4993-8f8f-4aa9b39422d5 · outbound

This paper cites Comprehensive deep learning-based framework for automatic organs-at-risk segmentation in head- and-neck and pelvis for MR-guided radiation therapy planning,.

Modality-Independent Explainable Detection of Inaccurate Organ Segmentations Using Denoising Autoencoders Comprehensive deep learning-based framework for automatic organs-at-risk segmentation in head- and-neck and pelvis for MR-guided radiation therapy planning,

Reference 13

Resolution: metadata mismatch; raw_fallback, observed 2026-08-16T12:39:12.679480Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 315c7b6b-bf9e-40d5-83ee-778ab32c4e1e · outbound

This paper cites U-Net: Convolutional Networks for Biomedical Image Segmentation,.

Modality-Independent Explainable Detection of Inaccurate Organ Segmentations Using Denoising Autoencoders U-Net: Convolutional Networks for Biomedical Image Segmentation,

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-16T12:39:12.375533Z

Source-reported events for the cited work

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Observation bc217f12-f460-4825-ae84-385e8d14fb3e · outbound

This paper cites Auto Segmentation.

Modality-Independent Explainable Detection of Inaccurate Organ Segmentations Using Denoising Autoencoders Auto Segmentation

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:39:12.724326Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation eeb7ec7f-08c9-4bd8-88f4-d1a74ac7fbc3 · outbound

This paper cites Rethinking the Inception Architecture for Computer Vision,.

Modality-Independent Explainable Detection of Inaccurate Organ Segmentations Using Denoising Autoencoders Rethinking the Inception Architecture for Computer Vision,

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-16T12:39:12.382924Z

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Observation 418556dd-0e1b-4d32-8e38-47f7f112608c · outbound

This paper cites CT-ORG, a new dataset for multiple organ segmentation in computed tomography,.

Modality-Independent Explainable Detection of Inaccurate Organ Segmentations Using Denoising Autoencoders CT-ORG, a new dataset for multiple organ segmentation in computed tomography,

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-16T12:39:12.386729Z

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Observation 8b10e660-fa6e-498f-be82-e8d8ba70ec0e · outbound

This paper cites Left-Ventricle Quantification Using Residual U- Net,.

Modality-Independent Explainable Detection of Inaccurate Organ Segmentations Using Denoising Autoencoders Left-Ventricle Quantification Using Residual U- Net,

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-16T12:39:12.390619Z

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Observation 42520b3d-72a2-4e74-9ff1-b9ccc0357233 · outbound

This paper cites Adam: A Method for Stochastic Optimization.

Modality-Independent Explainable Detection of Inaccurate Organ Segmentations Using Denoising Autoencoders Adam: A Method for Stochastic Optimization

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-16T12:39:12.394559Z

Source-reported events for the cited work

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Observation b481df87-b948-468c-abb7-2f43cbbda653 · outbound

This paper cites Very Deep VAEs Generalize Autoregressive Models and Can Outperform Them on Images.

Modality-Independent Explainable Detection of Inaccurate Organ Segmentations Using Denoising Autoencoders Very Deep VAEs Generalize Autoregressive Models and Can Outperform Them on Images

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-16T12:39:12.398705Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ca37031d-a68f-4eab-8096-25db6e831a8e · outbound

This paper cites Re-parameterizing VAEs for stability.

Modality-Independent Explainable Detection of Inaccurate Organ Segmentations Using Denoising Autoencoders Re-parameterizing VAEs for stability

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-16T12:39:12.402912Z

Source-reported events for the cited work

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Observation 11d384f6-13fc-4444-9b81-54d0b9870852 · outbound

This paper cites On the Generalized Distance in Statistics,.

Modality-Independent Explainable Detection of Inaccurate Organ Segmentations Using Denoising Autoencoders On the Generalized Distance in Statistics,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:39:12.709966Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 36165a8e-b068-49d9-8f90-c15d25cce96f · outbound

This paper cites Optimization for medical image segmentation: theory and practice when evaluating with dice score or jaccard index,.

Modality-Independent Explainable Detection of Inaccurate Organ Segmentations Using Denoising Autoencoders Optimization for medical image segmentation: theory and practice when evaluating with dice score or jaccard index,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:39:12.694400Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Pith citing papers

No inbound Pith citation observations are available.