Paper Citation Record · LEDGER

Uncertainty Awareness Enables Efficient Labeling for Cancer Subtyping in Digital Pathology

As of 8 August 2026, this Paper Citation Record lists 33 of 33 outbound references and 0 inbound Pith citation observations for arXiv:2506.11439.

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

pith.paper-citation-record.v1
2506.11439 v1

Coverage vector

measured 33 of 33 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T04:12:48.690881Z

measured 33 of 33 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+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

33 of 33 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation d22522f8-25ca-47bc-b815-30b400f65cc9 · outbound

This paper cites Emerg- ing properties in self-supervised vision transformers.

Uncertainty Awareness Enables Efficient Labeling for Cancer Subtyping in Digital Pathology Emerg- ing properties in self-supervised vision transformers

Reference 1

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Observation deff3549-5fac-4e42-89eb-d4a022729811 · outbound

This paper cites Active learning for patch-based digital pathology using convolutional neural networks to reduce annotation costs.

Uncertainty Awareness Enables Efficient Labeling for Cancer Subtyping in Digital Pathology Active learning for patch-based digital pathology using convolutional neural networks to reduce annotation costs

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:12:49.843737Z

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Observation 038d1b76-c5ab-407a-b190-220b00447106 · outbound

This paper cites A simple framework for contrastive learning of visual representations.

Uncertainty Awareness Enables Efficient Labeling for Cancer Subtyping in Digital Pathology A simple framework for contrastive learning of visual representations

Reference 3

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Observation 0ea725f1-50be-44b4-8fd1-0195f23ec4c1 · outbound

This paper cites Big self-supervised mod- els are strong semi-supervised learners.

Uncertainty Awareness Enables Efficient Labeling for Cancer Subtyping in Digital Pathology Big self-supervised mod- els are strong semi-supervised learners

Reference 4

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Observation cc089d6e-fce6-47f4-aa75-ccfb3ddc3d9d · outbound

This paper cites Exploring simple siamese rep- resentation learning.

Uncertainty Awareness Enables Efficient Labeling for Cancer Subtyping in Digital Pathology Exploring simple siamese rep- resentation learning

Reference 5

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Observation 1afc5160-e24b-4743-897b-610538d94610 · outbound

This paper cites An Empirical Study of Training Self-Supervised Vision Transformers.

Uncertainty Awareness Enables Efficient Labeling for Cancer Subtyping in Digital Pathology An Empirical Study of Training Self-Supervised Vision Transformers

Reference 6

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Observation 4392be63-0a89-4e25-8738-011bf6e4fabc · outbound

This paper cites Self super- vised contrastive learning for digital histopathology.

Uncertainty Awareness Enables Efficient Labeling for Cancer Subtyping in Digital Pathology Self super- vised contrastive learning for digital histopathology

Reference 7

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Observation fb30e21c-3916-473b-a41b-c819103a5d4c · outbound

This paper cites Upper and lower probabilities induced by a multivalued mapping.

Uncertainty Awareness Enables Efficient Labeling for Cancer Subtyping in Digital Pathology Upper and lower probabilities induced by a multivalued mapping

Reference 8

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Observation 8d5ad58b-3c31-4fa9-80eb-1eba032b647f · outbound

This paper cites Uncertainty-informed deep learn- ing models enable high-confidence predictions for digital histopathology.

Uncertainty Awareness Enables Efficient Labeling for Cancer Subtyping in Digital Pathology Uncertainty-informed deep learn- ing models enable high-confidence predictions for digital histopathology

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:12:49.770062Z

Source-reported events for the cited work

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Observation 81a72937-a7c5-4b9b-8672-793192849c34 · outbound

This paper cites Breast cancer histopathological image classification via deep active learning and confidence boosting.

Uncertainty Awareness Enables Efficient Labeling for Cancer Subtyping in Digital Pathology Breast cancer histopathological image classification via deep active learning and confidence boosting

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:12:49.758904Z

Source-reported events for the cited work

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Observation ee38ab6c-ecce-4b8b-97bf-d8616a5bcd24 · outbound

This paper cites Dropout as a bayesian approximation: Representing model uncertainty in deep learning.

Uncertainty Awareness Enables Efficient Labeling for Cancer Subtyping in Digital Pathology Dropout as a bayesian approximation: Representing model uncertainty in deep learning

Reference 11

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Observation e596fd1c-6a87-4a52-8518-f1c5fcf4d868 · outbound

This paper cites Learning representations by predicting bags of visual words.

Uncertainty Awareness Enables Efficient Labeling for Cancer Subtyping in Digital Pathology Learning representations by predicting bags of visual words

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:12:49.740343Z

Source-reported events for the cited work

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Observation 2838482e-ac97-4ace-8962-2489723d8eef · outbound

This paper cites Bootstrap your own latent-a new approach to self-supervised learning.

Uncertainty Awareness Enables Efficient Labeling for Cancer Subtyping in Digital Pathology Bootstrap your own latent-a new approach to self-supervised learning

Reference 13

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Observation 627e3140-87d4-49e2-a536-c4946601a1f6 · outbound

This paper cites Reliability-aware contrastive self-ensembling for semi-supervised medical image classifi- cation.

Uncertainty Awareness Enables Efficient Labeling for Cancer Subtyping in Digital Pathology Reliability-aware contrastive self-ensembling for semi-supervised medical image classifi- cation

Reference 14

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Observation 61aa5470-62e1-4e76-901f-18f4d73521de · outbound

This paper cites Masked autoencoders are scalable vision learners.

Uncertainty Awareness Enables Efficient Labeling for Cancer Subtyping in Digital Pathology Masked autoencoders are scalable vision learners

Reference 15

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Observation 288976ba-d864-4f29-b232-74ed719d2dde · outbound

This paper cites Distilling the Knowledge in a Neural Network.

Uncertainty Awareness Enables Efficient Labeling for Cancer Subtyping in Digital Pathology Distilling the Knowledge in a Neural Network

Reference 16

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Observation f4c1cf23-afb7-4c99-95bc-2892b30748bd · outbound

This paper cites Re- ducing the annotation cost of whole slide histology images using active learning.

Uncertainty Awareness Enables Efficient Labeling for Cancer Subtyping in Digital Pathology Re- ducing the annotation cost of whole slide histology images using active learning

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:12:49.691627Z

Source-reported events for the cited work

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Observation 6566841e-bef2-4405-b646-14ef50576d61 · outbound

This paper cites Histossl: Self-supervised representation learning for classi- fying histopathology images.

Uncertainty Awareness Enables Efficient Labeling for Cancer Subtyping in Digital Pathology Histossl: Self-supervised representation learning for classi- fying histopathology images

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:12:49.681558Z

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Observation e70a7e91-718a-447e-af79-f6e6d7d73887 · outbound

This paper cites Generalising bayes’ theorem in subjective logic.

Uncertainty Awareness Enables Efficient Labeling for Cancer Subtyping in Digital Pathology Generalising bayes’ theorem in subjective logic

Reference 19

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Observation 662464aa-6450-439f-a125-c1897efb5841 · outbound

This paper cites 100,000 histological images of human colorectal cancer and healthy tissue.

Uncertainty Awareness Enables Efficient Labeling for Cancer Subtyping in Digital Pathology 100,000 histological images of human colorectal cancer and healthy tissue

Reference 20

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Observation 601a4a8f-3b2d-4cde-8453-8fbe0b5014d6 · outbound

This paper cites Continuous mul- tivariate distributions–vol.

Uncertainty Awareness Enables Efficient Labeling for Cancer Subtyping in Digital Pathology Continuous mul- tivariate distributions–vol

Reference 21

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Source-reported events for the cited work

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Observation ee3e0eab-0d2d-4d06-9320-d0cb38975dd3 · outbound

This paper cites Simple and scalable predictive uncertainty estima- tion using deep ensembles.

Uncertainty Awareness Enables Efficient Labeling for Cancer Subtyping in Digital Pathology Simple and scalable predictive uncertainty estima- tion using deep ensembles

Reference 22

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Observation dfa35c4e-30b0-485a-a8f9-f7dc7da7410c · outbound

This paper cites Self-distillation Augmented Masked Autoencoders for Histopathological Image Classification.

Uncertainty Awareness Enables Efficient Labeling for Cancer Subtyping in Digital Pathology Self-distillation Augmented Masked Autoencoders for Histopathological Image Classification

Reference 23

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Observation a27451a2-20ac-4702-b755-6051809c0bf9 · outbound

This paper cites Self-supervised learning of pretext-invariant representations.

Uncertainty Awareness Enables Efficient Labeling for Cancer Subtyping in Digital Pathology Self-supervised learning of pretext-invariant representations

Reference 24

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Observation d0b8b0b0-3aaf-4602-97cf-49ab26a8f815 · outbound

This paper cites Eviden- tial deep learning to quantify classification uncertainty.

Uncertainty Awareness Enables Efficient Labeling for Cancer Subtyping in Digital Pathology Eviden- tial deep learning to quantify classification uncertainty

Reference 25

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Observation 218c65f2-ef04-40b4-a52c-641ae3ddfa91 · outbound

This paper cites FusDom: Combining In-Domain and Out-of-Domain Knowledge for Continuous Self-Supervised Learning.

Uncertainty Awareness Enables Efficient Labeling for Cancer Subtyping in Digital Pathology FusDom: Combining In-Domain and Out-of-Domain Knowledge for Continuous Self-Supervised Learning

Reference 26

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Observation cc0574da-afa0-4f82-95a4-8bdca7b1d066 · outbound

This paper cites From theories to queries: Active learning in practice.

Uncertainty Awareness Enables Efficient Labeling for Cancer Subtyping in Digital Pathology From theories to queries: Active learning in practice

Reference 27

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Observation e742e2e2-1c69-47a4-be58-6d7a3802e956 · outbound

This paper cites What makes for good views for contrastive learning? Advances in neural informa- tion processing systems, 33:6827–6839, 2020.

Uncertainty Awareness Enables Efficient Labeling for Cancer Subtyping in Digital Pathology What makes for good views for contrastive learning? Advances in neural informa- tion processing systems, 33:6827–6839, 2020

Reference 28

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Observation 31e4411c-706e-4a2c-b434-984d853fc244 · outbound

This paper cites Rotation equivariant cnns for digital pathology.

Uncertainty Awareness Enables Efficient Labeling for Cancer Subtyping in Digital Pathology Rotation equivariant cnns for digital pathology

Reference 29

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Observation 07243211-e72e-4f6d-894a-e34ebbe0183e · outbound

This paper cites Fast dropout training.

Uncertainty Awareness Enables Efficient Labeling for Cancer Subtyping in Digital Pathology Fast dropout training

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:12:49.026347Z

Source-reported events for the cited work

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Observation 4950ce37-a1df-4856-a481-3f19cd62de6f · outbound

This paper cites Transpath: Transformer-based self-supervised learning for histopatho- logical image classification.

Uncertainty Awareness Enables Efficient Labeling for Cancer Subtyping in Digital Pathology Transpath: Transformer-based self-supervised learning for histopatho- logical image classification

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:12:48.955115Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation a8ea59d3-31ef-4375-97ba-ac9d3e9d2aac · outbound

This paper cites Hyperparameter ensembles for robustness and un- certainty quantification.

Uncertainty Awareness Enables Efficient Labeling for Cancer Subtyping in Digital Pathology Hyperparameter ensembles for robustness and un- certainty quantification

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:12:48.885519Z

Source-reported events for the cited work

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Observation d425148c-c8d8-4812-b134-bf0d189ca242 · outbound

This paper cites Barlow twins: Self-supervised learning via redundancy reduction.

Uncertainty Awareness Enables Efficient Labeling for Cancer Subtyping in Digital Pathology Barlow twins: Self-supervised learning via redundancy reduction

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:12:48.791061Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Pith citing papers

No inbound Pith citation observations are available.