Paper Citation Record · LEDGER

Enhancing Uncertainty Estimation in Semantic Segmentation via Monte-Carlo Frequency Dropout

As of 18 August 2026, this Paper Citation Record lists 28 of 28 outbound references and 1 inbound Pith citation observation for arXiv:2501.11258.

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

pith.paper-citation-record.v1
2501.11258 v1

Coverage vector

measured 28 of 28 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T18:32:12.349691Z

measured 29 of 29 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T18:32:12.253415Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-10T18:32:12.399038Z

Reference resolution

28 of 28 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation bf603554-17bc-4b17-aa34-21522aeaf3ad · outbound

This paper cites Enhancing Uncertainty Estimation in Semantic Segmentation via Monte-Carlo Frequency Dropout.

Enhancing Uncertainty Estimation in Semantic Segmentation via Monte-Carlo Frequency Dropout Enhancing Uncertainty Estimation in Semantic Segmentation via Monte-Carlo Frequency Dropout

Reference 1

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Observation 708678e5-f9fc-4b21-b4d1-380464e7ecb5 · outbound

This paper cites an unresolved cited work.

Enhancing Uncertainty Estimation in Semantic Segmentation via Monte-Carlo Frequency Dropout Unresolved cited work

Reference 2

Resolution: unresolved; raw_fallback, observed 2026-08-10T18:32:12.639529Z

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Observation f2d27c19-8505-4e29-a417-07cdcf8a4681 · outbound

This paper cites The CT and MRI datasets were sourced from the Medical Segmentation Decathlon [12].

Enhancing Uncertainty Estimation in Semantic Segmentation via Monte-Carlo Frequency Dropout The CT and MRI datasets were sourced from the Medical Segmentation Decathlon [12]

Reference 3

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Observation 117619cc-9f61-472e-9150-35a3168d85cc · outbound

This paper cites an unresolved cited work.

Enhancing Uncertainty Estimation in Semantic Segmentation via Monte-Carlo Frequency Dropout Unresolved cited work

Reference 4

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Observation b033e5b1-2246-4acd-9bae-ed84cf7e8df0 · outbound

This paper cites MC simulations generated uncertainty estimates to identify segmentation er- rors in tasks challenging state-of-the-art models.

Enhancing Uncertainty Estimation in Semantic Segmentation via Monte-Carlo Frequency Dropout MC simulations generated uncertainty estimates to identify segmentation er- rors in tasks challenging state-of-the-art models

Reference 5

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Observation 5178e595-20fa-438b-acfa-fcfe543187bd · outbound

This paper cites an unresolved cited work.

Enhancing Uncertainty Estimation in Semantic Segmentation via Monte-Carlo Frequency Dropout Unresolved cited work

Reference 6

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Observation 194d88fb-2129-4865-8c76-13389014a7bd · outbound

This paper cites The authors have no relevant financial or non-financial interests to dis- close.

Enhancing Uncertainty Estimation in Semantic Segmentation via Monte-Carlo Frequency Dropout The authors have no relevant financial or non-financial interests to dis- close

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:32:12.590194Z

Source-reported events for the cited work

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Observation f2affa3c-facd-4d3d-8e4a-25e25530a018 · outbound

This paper cites A review of uncertainty quantification in med- ical image analysis: probabilistic and non-probabilistic methods,.

Enhancing Uncertainty Estimation in Semantic Segmentation via Monte-Carlo Frequency Dropout A review of uncertainty quantification in med- ical image analysis: probabilistic and non-probabilistic methods,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:32:12.580111Z

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Observation 8c412809-5ec5-4b19-aac6-4abbb6b01c1d · outbound

This paper cites Aleatoric uncertainty estimation with test-time augmentation for medical image segmentation with convolutional neural networks,.

Enhancing Uncertainty Estimation in Semantic Segmentation via Monte-Carlo Frequency Dropout Aleatoric uncertainty estimation with test-time augmentation for medical image segmentation with convolutional neural networks,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:32:12.569209Z

Source-reported events for the cited work

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Observation e26cda18-e097-4386-afb6-f821c21cff43 · outbound

This paper cites Automatic brain tumor segmentation us- ing convolutional neural networks with test-time aug- mentation,.

Enhancing Uncertainty Estimation in Semantic Segmentation via Monte-Carlo Frequency Dropout Automatic brain tumor segmentation us- ing convolutional neural networks with test-time aug- mentation,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:32:12.557321Z

Source-reported events for the cited work

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Observation 6c316889-c6f1-4571-93e2-336456e8bdf3 · outbound

This paper cites Accurate and ro- bust deep learning-based segmentation of the prostate clinical target volume in ultrasound images,.

Enhancing Uncertainty Estimation in Semantic Segmentation via Monte-Carlo Frequency Dropout Accurate and ro- bust deep learning-based segmentation of the prostate clinical target volume in ultrasound images,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:32:12.547038Z

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Observation 7d74a0b1-eb78-4656-ae58-1b693db9de24 · outbound

This paper cites Exploring uncertainty measures in bayesian deep attentive neural networks for prostate zonal segmentation,.

Enhancing Uncertainty Estimation in Semantic Segmentation via Monte-Carlo Frequency Dropout Exploring uncertainty measures in bayesian deep attentive neural networks for prostate zonal segmentation,

Reference 12

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Observation 52749619-5666-4443-bc64-eedde3132ab2 · outbound

This paper cites Exploring uncertainty measures in deep net- works for multiple sclerosis lesion detection and seg- mentation,.

Enhancing Uncertainty Estimation in Semantic Segmentation via Monte-Carlo Frequency Dropout Exploring uncertainty measures in deep net- works for multiple sclerosis lesion detection and seg- mentation,

Reference 13

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Observation f322a3bb-4066-43ed-8b8a-41fdb9b8f1bb · outbound

This paper cites Regularization of neural networks using dropconnect,.

Enhancing Uncertainty Estimation in Semantic Segmentation via Monte-Carlo Frequency Dropout Regularization of neural networks using dropconnect,

Reference 14

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Source-reported events for the cited work

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Observation b484425e-5d16-45d6-8886-d42277363538 · outbound

This paper cites Dropout: a simple way to prevent neural networks from overfit- ting,.

Enhancing Uncertainty Estimation in Semantic Segmentation via Monte-Carlo Frequency Dropout Dropout: a simple way to prevent neural networks from overfit- ting,

Reference 15

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Observation e7acd5f8-b3c3-4697-8d1d-5ab007223030 · outbound

This paper cites Dropout as a Bayesian approximation: Representing model uncer- tainty in deep learning,.

Enhancing Uncertainty Estimation in Semantic Segmentation via Monte-Carlo Frequency Dropout Dropout as a Bayesian approximation: Representing model uncer- tainty in deep learning,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:32:12.500047Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 9a590cc1-be08-48a8-8b53-b5462d9893a8 · outbound

This paper cites Monte-carlo frequency dropout for predictive uncertainty estimation in deep learning,.

Enhancing Uncertainty Estimation in Semantic Segmentation via Monte-Carlo Frequency Dropout Monte-carlo frequency dropout for predictive uncertainty estimation in deep learning,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:32:12.489831Z

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Observation c42336bc-35ad-4e27-a341-2382ba888f36 · outbound

This paper cites Regularization of deep neural networks with spectral dropout,.

Enhancing Uncertainty Estimation in Semantic Segmentation via Monte-Carlo Frequency Dropout Regularization of deep neural networks with spectral dropout,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:32:12.478651Z

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Observation 70f6b051-91e2-4823-b955-586c1967c76e · outbound

This paper cites The medical seg- mentation decathlon,.

Enhancing Uncertainty Estimation in Semantic Segmentation via Monte-Carlo Frequency Dropout The medical seg- mentation decathlon,

Reference 19

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Observation 39fe087f-36cf-42c1-8fa3-a0dd8b964a20 · outbound

This paper cites Lung segmentation in chest radiographs using anatomical atlases with nonrigid reg- istration,.

Enhancing Uncertainty Estimation in Semantic Segmentation via Monte-Carlo Frequency Dropout Lung segmentation in chest radiographs using anatomical atlases with nonrigid reg- istration,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:32:12.462084Z

Source-reported events for the cited work

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Observation fc96dc0f-8c0f-4af7-a22a-4ac0ab8e7eed · outbound

This paper cites Automatic tuberculosis screening using chest radiographs,.

Enhancing Uncertainty Estimation in Semantic Segmentation via Monte-Carlo Frequency Dropout Automatic tuberculosis screening using chest radiographs,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:32:12.451472Z

Source-reported events for the cited work

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Observation 49eaafa2-788e-40fc-9adf-1b0cc87ef111 · outbound

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

Enhancing Uncertainty Estimation in Semantic Segmentation via Monte-Carlo Frequency Dropout nnu-net: a self- configuring method for deep learning-based biomedical image segmentation,

Reference 22

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Observation ccfa47d2-a912-4b18-a7b4-2cf328b678dd · outbound

This paper cites Segment anything in medical images,.

Enhancing Uncertainty Estimation in Semantic Segmentation via Monte-Carlo Frequency Dropout Segment anything in medical images,

Reference 23

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Source-reported events for the cited work

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Observation a7136ed6-186d-412f-b3d5-fef7ebc9b33b · outbound

This paper cites Segment anything,.

Enhancing Uncertainty Estimation in Semantic Segmentation via Monte-Carlo Frequency Dropout Segment anything,

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-10T18:32:12.335611Z

Source-reported events for the cited work

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Observation 145c9052-9377-487b-8da3-0771d3e1f827 · outbound

This paper cites Evaluating Bayesian Deep Learning Methods for Semantic Segmentation.

Enhancing Uncertainty Estimation in Semantic Segmentation via Monte-Carlo Frequency Dropout Evaluating Bayesian Deep Learning Methods for Semantic Segmentation

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-10T18:32:12.339014Z

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Observation f4bd6331-afce-4ccd-b231-4777771cfb3c · outbound

This paper cites Dropconnect is effective in modeling uncertainty of bayesian deep networks,.

Enhancing Uncertainty Estimation in Semantic Segmentation via Monte-Carlo Frequency Dropout Dropconnect is effective in modeling uncertainty of bayesian deep networks,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:32:12.422976Z

Source-reported events for the cited work

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Observation efba4fb0-e700-4e5c-a6ae-b97e6cdddaeb · outbound

This paper cites Well-calibrated Model Uncertainty with Temperature Scaling for Dropout Variational Inference.

Enhancing Uncertainty Estimation in Semantic Segmentation via Monte-Carlo Frequency Dropout Well-calibrated Model Uncertainty with Temperature Scaling for Dropout Variational Inference

Reference 27

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Source-reported events for the cited work

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Observation 1c54c98e-87e7-4174-82c6-f1b2424ab492 · outbound

This paper cites On calibration of modern neural networks,.

Enhancing Uncertainty Estimation in Semantic Segmentation via Monte-Carlo Frequency Dropout On calibration of modern neural networks,

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-10T18:32:12.349691Z

Source-reported events for the cited work

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Pith citing papers

Observation bf603554-17bc-4b17-aa34-21522aeaf3ad · inbound