Paper Citation Record · LEDGER

EigenRank by Committee: A Data Subset Selection and Failure Prediction paradigm for Robust Deep Learning based Medical Image Segmentation

As of 16 August 2026, this Paper Citation Record lists 35 of 35 outbound references and 0 inbound Pith citation observations for arXiv:1908.06337.

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

pith.paper-citation-record.v1
1908.06337 v2

Coverage vector

measured 35 of 35 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-14T12:54:04.174982Z

measured 35 of 35 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+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

35 of 35 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation f36f0100-d1b5-41ae-9833-5370284cc1e1 · outbound

This paper cites Tensorﬂow: a system for large-scale machine learning.

EigenRank by Committee: A Data Subset Selection and Failure Prediction paradigm for Robust Deep Learning based Medical Image Segmentation Tensorﬂow: a system for large-scale machine learning

Reference 1

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Observation e707a883-4a92-44d7-9059-82c30e2aed75 · outbound

This paper cites Annotation-cost Minimization for Medical Image Segmentation using Suggestive Mixed Supervision Fully Convolutional Networks.

EigenRank by Committee: A Data Subset Selection and Failure Prediction paradigm for Robust Deep Learning based Medical Image Segmentation Annotation-cost Minimization for Medical Image Segmentation using Suggestive Mixed Supervision Fully Convolutional Networks

Reference 2

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Observation eac664fe-d634-4ec5-97c4-db73357224f7 · outbound

This paper cites Bhatia, Positive deﬁnite matrices.

EigenRank by Committee: A Data Subset Selection and Failure Prediction paradigm for Robust Deep Learning based Medical Image Segmentation Bhatia, Positive deﬁnite matrices

Reference 3

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This paper cites Sensitive quantitative predictions of peptide-mhc binding by a ‘query by com- mittee’artiﬁcial neural network approach,.

EigenRank by Committee: A Data Subset Selection and Failure Prediction paradigm for Robust Deep Learning based Medical Image Segmentation Sensitive quantitative predictions of peptide-mhc binding by a ‘query by com- mittee’artiﬁcial neural network approach,

Reference 4

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This paper cites Chollet et al., “Keras,” 2015.

EigenRank by Committee: A Data Subset Selection and Failure Prediction paradigm for Robust Deep Learning based Medical Image Segmentation Chollet et al., “Keras,” 2015

Reference 5

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This paper cites Incorpo- rating expert feedback into active anomaly discovery,.

EigenRank by Committee: A Data Subset Selection and Failure Prediction paradigm for Robust Deep Learning based Medical Image Segmentation Incorpo- rating expert feedback into active anomaly discovery,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:54:04.807748Z

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Observation e48b643f-ed16-472d-9d18-a4f7cd853d0b · outbound

This paper cites A Strategy of MR Brain Tissue Images' Suggestive Annotation Based on Modified U-Net.

EigenRank by Committee: A Data Subset Selection and Failure Prediction paradigm for Robust Deep Learning based Medical Image Segmentation A Strategy of MR Brain Tissue Images' Suggestive Annotation Based on Modified U-Net

Reference 7

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This paper cites Leveraging Uncertainty Estimates for Predicting Segmentation Quality.

EigenRank by Committee: A Data Subset Selection and Failure Prediction paradigm for Robust Deep Learning based Medical Image Segmentation Leveraging Uncertainty Estimates for Predicting Segmentation Quality

Reference 8

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Observation 216b8923-0d44-4e72-b3af-384e24eb88a7 · outbound

This paper cites Deep Active Learning for Axon-Myelin Segmentation on Histology Data.

EigenRank by Committee: A Data Subset Selection and Failure Prediction paradigm for Robust Deep Learning based Medical Image Segmentation Deep Active Learning for Axon-Myelin Segmentation on Histology Data

Reference 9

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Observation c831d884-d178-4850-a560-b5eb8b63b685 · outbound

This paper cites Deep CNN ensembles and suggestive annotations for infant brain MRI segmentation.

EigenRank by Committee: A Data Subset Selection and Failure Prediction paradigm for Robust Deep Learning based Medical Image Segmentation Deep CNN ensembles and suggestive annotations for infant brain MRI segmentation

Reference 10

Resolution: verified exact; local_arxiv, observed 2026-08-14T12:54:04.313945Z

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Observation 114eaf8d-1179-43bf-8b8e-6d1a6403eb04 · outbound

This paper cites Selective sampling using the query by committee algorithm,.

EigenRank by Committee: A Data Subset Selection and Failure Prediction paradigm for Robust Deep Learning based Medical Image Segmentation Selective sampling using the query by committee algorithm,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:54:04.791850Z

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Observation d00b421f-6d56-4c9c-85d9-29b754ab205d · outbound

This paper cites Extreme Augmentation : Can deep learning based medical image segmentation be trained using a single manually delineated scan?.

EigenRank by Committee: A Data Subset Selection and Failure Prediction paradigm for Robust Deep Learning based Medical Image Segmentation Extreme Augmentation : Can deep learning based medical image segmentation be trained using a single manually delineated scan?

Reference 12

Resolution: verified exact; local_arxiv, observed 2026-08-14T12:54:04.283959Z

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Observation 7536f309-e68b-4716-a4ce-ea902fb8e442 · outbound

This paper cites Deep learning in the small sample size setting: cascaded feed forward neural net- works for medical image segmentation,.

EigenRank by Committee: A Data Subset Selection and Failure Prediction paradigm for Robust Deep Learning based Medical Image Segmentation Deep learning in the small sample size setting: cascaded feed forward neural net- works for medical image segmentation,

Reference 13

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Observation 66bff03a-dd95-4b03-a378-b79de312c0b5 · outbound

This paper cites Query by committee made real,.

EigenRank by Committee: A Data Subset Selection and Failure Prediction paradigm for Robust Deep Learning based Medical Image Segmentation Query by committee made real,

Reference 14

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Observation 6c968a5c-0bfa-4a46-9a53-8b303dfb63ad · outbound

This paper cites Explaining and Harnessing Adversarial Examples.

EigenRank by Committee: A Data Subset Selection and Failure Prediction paradigm for Robust Deep Learning based Medical Image Segmentation Explaining and Harnessing Adversarial Examples

Reference 15

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Observation af343892-2e39-47a8-b1b3-398719a709ed · outbound

This paper cites Guest editorial deep learning in medical imaging: Overview and future promise of an exciting new technique,.

EigenRank by Committee: A Data Subset Selection and Failure Prediction paradigm for Robust Deep Learning based Medical Image Segmentation Guest editorial deep learning in medical imaging: Overview and future promise of an exciting new technique,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:54:04.743466Z

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Observation b55e2b81-8ab6-489c-a9ef-3d7af5a7dab9 · outbound

This paper cites Assessing Reliability and Challenges of Uncertainty Estimations for Medical Image Segmentation.

EigenRank by Committee: A Data Subset Selection and Failure Prediction paradigm for Robust Deep Learning based Medical Image Segmentation Assessing Reliability and Challenges of Uncertainty Estimations for Medical Image Segmentation

Reference 17

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This paper cites Medical image ﬁle formats,.

EigenRank by Committee: A Data Subset Selection and Failure Prediction paradigm for Robust Deep Learning based Medical Image Segmentation Medical image ﬁle formats,

Reference 18

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Observation 732e8cc8-46d1-4d38-ac4b-2346792ef175 · outbound

This paper cites The ﬁrst step for neuroimaging data analysis: DICOM to NIfTI conversion,.

EigenRank by Committee: A Data Subset Selection and Failure Prediction paradigm for Robust Deep Learning based Medical Image Segmentation The ﬁrst step for neuroimaging data analysis: DICOM to NIfTI conversion,

Reference 19

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Observation e762a341-48b8-4735-974a-403c1c07c5ec · outbound

This paper cites The design of simpleitk,.

EigenRank by Committee: A Data Subset Selection and Failure Prediction paradigm for Robust Deep Learning based Medical Image Segmentation The design of simpleitk,

Reference 20

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Observation b4243f61-fe6c-48cf-819f-8a9d3dd5243a · outbound

This paper cites On the positive semi-deﬁnite property of similarity matrices,.

EigenRank by Committee: A Data Subset Selection and Failure Prediction paradigm for Robust Deep Learning based Medical Image Segmentation On the positive semi-deﬁnite property of similarity matrices,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:54:04.674948Z

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Observation 88c290f1-aa71-4f2c-9e7a-9066629e9db7 · outbound

This paper cites Deep neural networks are easily fooled: High conﬁdence predictions for unrecognizable images,.

EigenRank by Committee: A Data Subset Selection and Failure Prediction paradigm for Robust Deep Learning based Medical Image Segmentation Deep neural networks are easily fooled: High conﬁdence predictions for unrecognizable images,

Reference 22

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Observation c6bbc9fe-ae2c-48c9-b6a6-2f2e582d3536 · outbound

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

EigenRank by Committee: A Data Subset Selection and Failure Prediction paradigm for Robust Deep Learning based Medical Image Segmentation U-Net: Convolutional Networks for Biomedical Image Segmentation,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:54:04.642842Z

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This paper cites Active learn- ing in recommender systems,.

EigenRank by Committee: A Data Subset Selection and Failure Prediction paradigm for Robust Deep Learning based Medical Image Segmentation Active learn- ing in recommender systems,

Reference 24

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Observation 559ab0e2-92f2-4d75-9adc-ac1323aa3e6f · outbound

This paper cites apricot: Submodular selection for data summarization in Python.

EigenRank by Committee: A Data Subset Selection and Failure Prediction paradigm for Robust Deep Learning based Medical Image Segmentation apricot: Submodular selection for data summarization in Python

Reference 25

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EigenRank by Committee: A Data Subset Selection and Failure Prediction paradigm for Robust Deep Learning based Medical Image Segmentation Active learning literature survey,

Reference 26

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This paper cites Query by committee,.

EigenRank by Committee: A Data Subset Selection and Failure Prediction paradigm for Robust Deep Learning based Medical Image Segmentation Query by committee,

Reference 27

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This paper cites Active deep learning with ﬁsher information for patch-wise semantic segmentation,.

EigenRank by Committee: A Data Subset Selection and Failure Prediction paradigm for Robust Deep Learning based Medical Image Segmentation Active deep learning with ﬁsher information for patch-wise semantic segmentation,

Reference 28

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This paper cites Current procedural terminology (CPT).

EigenRank by Committee: A Data Subset Selection and Failure Prediction paradigm for Robust Deep Learning based Medical Image Segmentation Current procedural terminology (CPT)

Reference 29

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This paper cites Submodularity in data subset selection and active learning,.

EigenRank by Committee: A Data Subset Selection and Failure Prediction paradigm for Robust Deep Learning based Medical Image Segmentation Submodularity in data subset selection and active learning,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:54:04.527692Z

Source-reported events for the cited work

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Observation 370b8603-3025-4a50-85c3-fae803dfd126 · outbound

This paper cites Suggestive annotation: A deep active learning framework for biomedical image segmentation,.

EigenRank by Committee: A Data Subset Selection and Failure Prediction paradigm for Robust Deep Learning based Medical Image Segmentation Suggestive annotation: A deep active learning framework for biomedical image segmentation,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:54:04.510799Z

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Observation 8bee8ea9-cea3-4530-9d97-4de5ca96e420 · outbound

This paper cites User-guided 3D active contour segmentation of anatomical structures: Signiﬁcantly improved efﬁciency and reliabil- ity,.

EigenRank by Committee: A Data Subset Selection and Failure Prediction paradigm for Robust Deep Learning based Medical Image Segmentation User-guided 3D active contour segmentation of anatomical structures: Signiﬁcantly improved efﬁciency and reliabil- ity,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:54:04.488470Z

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This paper cites Road extraction by deep residual u-net,.

EigenRank by Committee: A Data Subset Selection and Failure Prediction paradigm for Robust Deep Learning based Medical Image Segmentation Road extraction by deep residual u-net,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:54:04.468605Z

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Observation 0fea2eab-77a8-44f3-9434-7473663c52f0 · outbound

This paper cites Deep learning based instance segmentation in 3d biomedical images using weak annotation,.

EigenRank by Committee: A Data Subset Selection and Failure Prediction paradigm for Robust Deep Learning based Medical Image Segmentation Deep learning based instance segmentation in 3d biomedical images using weak annotation,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:54:04.452617Z

Source-reported events for the cited work

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Observation 6bffd6b4-a386-4ff5-b056-1366be1c7ba0 · outbound

This paper cites A brief introduction to weakly supervised learning,.

EigenRank by Committee: A Data Subset Selection and Failure Prediction paradigm for Robust Deep Learning based Medical Image Segmentation A brief introduction to weakly supervised learning,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:54:04.435395Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Pith citing papers

No inbound Pith citation observations are available.