Paper Citation Record · LEDGER

Explaining Digital Pathology Models via Clustering Activations

As of 8 August 2026, this Paper Citation Record lists 26 of 26 outbound references and 1 inbound Pith citation observation for arXiv:2511.14558.

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

pith.paper-citation-record.v1
2511.14558 v2

Coverage vector

measured 26 of 26 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-03T21:37:24.354112Z

measured 27 of 27 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-03T21:37:22.033807Z

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

verified exact0
verified fuzzy0
unresolved25
parse uncertain0
malformed identifier1
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 4b8ffbcf-d476-4748-9e42-aa280c02b9c4 · outbound

This paper cites Explaining Digital Pathology Models via Clustering Activations.

Explaining Digital Pathology Models via Clustering Activations Explaining Digital Pathology Models via Clustering Activations

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-03T21:37:22.033807Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 13234a03-2c94-42ad-b796-820fed2dbd5c · outbound

This paper cites an unresolved cited work.

Explaining Digital Pathology Models via Clustering Activations Unresolved cited work

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-03T21:37:22.102079Z

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Unavailable: canonical work link unavailable.

Observation 7203d077-3ec1-4e02-a40d-af3bc4734358 · outbound

This paper cites 1 shows a typical output of our model forK= 6, where each of the classes is shown in a different color and classes are overlaid over the top of each other.

Explaining Digital Pathology Models via Clustering Activations 1 shows a typical output of our model forK= 6, where each of the classes is shown in a different color and classes are overlaid over the top of each other

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-03T21:37:22.179702Z

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Observation b11c27ef-d490-4624-b54b-3f6ed9c5b079 · outbound

This paper cites an unresolved cited work.

Explaining Digital Pathology Models via Clustering Activations Unresolved cited work

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-03T21:37:22.226076Z

Source-reported events for the cited work

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Observation 179f1884-0ed1-49ed-bc6b-11e4105e489c · outbound

This paper cites an unresolved cited work.

Explaining Digital Pathology Models via Clustering Activations Unresolved cited work

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-03T21:37:22.298025Z

Source-reported events for the cited work

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Observation 0e4f68c1-9326-4f39-87ef-7d7146ab8382 · outbound

This paper cites an unresolved cited work.

Explaining Digital Pathology Models via Clustering Activations Unresolved cited work

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-03T21:37:22.338527Z

Source-reported events for the cited work

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Observation b67cb8cc-e72d-4e39-b33d-3ddfd687dd6a · outbound

This paper cites an unresolved cited work.

Explaining Digital Pathology Models via Clustering Activations Unresolved cited work

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-03T21:37:22.405285Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 03e5c788-0053-4c82-a62f-4d175f5f828a · outbound

This paper cites an unresolved cited work.

Explaining Digital Pathology Models via Clustering Activations Unresolved cited work

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-03T21:37:22.444355Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0264d555-ada4-4b92-9481-cebcb61581c2 · outbound

This paper cites This suggests that the classes correspond to observable mor- phological structures.

Explaining Digital Pathology Models via Clustering Activations This suggests that the classes correspond to observable mor- phological structures

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-03T21:37:22.525405Z

Source-reported events for the cited work

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Observation 6a42c8f3-a833-4c3f-8cc2-7ab3119b388c · outbound

This paper cites Unlike commonly used techniques such as Grad- CAM, the provided explanations focus on the global behavior of the model.

Explaining Digital Pathology Models via Clustering Activations Unlike commonly used techniques such as Grad- CAM, the provided explanations focus on the global behavior of the model

Reference 10

Resolution: malformed identifier; no resolver link, observed 2026-08-03T21:37:22.630100Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 16134a4b-e090-4a6a-97d8-8485895eec89 · outbound

This paper cites MOU 385 920.

Explaining Digital Pathology Models via Clustering Activations MOU 385 920

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-03T21:37:22.710222Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1c70dd7b-b992-400b-b3bf-fbbd53ced4f6 · outbound

This paper cites 101079183 (BioMedAI TWINNING).

Explaining Digital Pathology Models via Clustering Activations 101079183 (BioMedAI TWINNING)

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-03T21:37:22.753124Z

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Observation b735ee0a-ac35-41a9-af64-149c4c3ba8a1 · outbound

This paper cites Glea- son grading: past, present and future,.

Explaining Digital Pathology Models via Clustering Activations Glea- son grading: past, present and future,

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-03T21:37:22.912840Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f006579c-185c-4a5c-bfe9-25a27d7b83d5 · outbound

This paper cites Clinical-grade computational pathology using weakly supervised deep learning on whole slide images,.

Explaining Digital Pathology Models via Clustering Activations Clinical-grade computational pathology using weakly supervised deep learning on whole slide images,

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-03T21:37:23.012793Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9b9a5d6e-689e-43a1-bfb1-ab4da5d2479f · outbound

This paper cites A survey on deep learning in medical image analysis,.

Explaining Digital Pathology Models via Clustering Activations A survey on deep learning in medical image analysis,

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-03T21:37:23.114842Z

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Observation 45488000-cfe5-4ec5-8759-aed4f67f3730 · outbound

This paper cites Explainable artificial intelligence (XAI) in deep learning-based medical image analysis,.

Explaining Digital Pathology Models via Clustering Activations Explainable artificial intelligence (XAI) in deep learning-based medical image analysis,

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-03T21:37:23.234325Z

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Unavailable: canonical work link unavailable.

Observation c83dc8a9-66ba-441a-8bfc-fcbcd1398522 · outbound

This paper cites Re- solving challenges in deep learning-based analyses of histopathological images using explanation methods,.

Explaining Digital Pathology Models via Clustering Activations Re- solving challenges in deep learning-based analyses of histopathological images using explanation methods,

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-03T21:37:23.367446Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 31f13ab3-73eb-4c62-8a7b-0e02833af916 · outbound

This paper cites A survey on explainable artificial intelligence (XAI) techniques for visualizing deep learning models in medical imaging,.

Explaining Digital Pathology Models via Clustering Activations A survey on explainable artificial intelligence (XAI) techniques for visualizing deep learning models in medical imaging,

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-03T21:37:23.439883Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7cbaf1f6-bcec-4c6d-9795-0e7812a3cfad · outbound

This paper cites Explainabil- ity and causability in digital pathology,.

Explaining Digital Pathology Models via Clustering Activations Explainabil- ity and causability in digital pathology,

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-03T21:37:23.537624Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 87216d00-53c1-4778-9cdd-059e34cceb8c · outbound

This paper cites Samek, G.

Explaining Digital Pathology Models via Clustering Activations Samek, G

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-03T21:37:23.616846Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cfbde70a-62aa-4f53-8810-9730c607e73d · outbound

This paper cites Shedding light on the black box of a neural network used to de- tect prostate cancer in whole slide images by occlusion- based explainability,.

Explaining Digital Pathology Models via Clustering Activations Shedding light on the black box of a neural network used to de- tect prostate cancer in whole slide images by occlusion- based explainability,

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-03T21:37:23.747515Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d394de8a-8c27-4d90-8ebf-d40a38dcdad0 · outbound

This paper cites Grad- CAM: Visual explanations from deep networks via gradient-based localization,.

Explaining Digital Pathology Models via Clustering Activations Grad- CAM: Visual explanations from deep networks via gradient-based localization,

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-03T21:37:23.837646Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 28c37ff5-534a-47f1-b75e-58269ac755bd · outbound

This paper cites Use HiResCAM instead of Grad-CAM for faithful explanations of convolutional neural networks.

Explaining Digital Pathology Models via Clustering Activations Use HiResCAM instead of Grad-CAM for faithful explanations of convolutional neural networks

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-03T21:37:23.974162Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 59d492b0-b5c8-46e3-9dea-8f29600e14ca · outbound

This paper cites Montavon, A.

Explaining Digital Pathology Models via Clustering Activations Montavon, A

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-03T21:37:24.114337Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0852fc87-deac-4996-a1dc-78876a65734d · outbound

This paper cites Deep Feature Factorization for Concept Discovery,.

Explaining Digital Pathology Models via Clustering Activations Deep Feature Factorization for Concept Discovery,

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-03T21:37:24.171542Z

Source-reported events for the cited work

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Observation de9d4795-d546-43b5-b473-43211085bc2e · outbound

This paper cites Segmentation by Factorization: Unsupervised Semantic Segmentation for Pathology by Factorizing Foundation Model Features.

Explaining Digital Pathology Models via Clustering Activations Segmentation by Factorization: Unsupervised Semantic Segmentation for Pathology by Factorizing Foundation Model Features

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-03T21:37:24.354112Z

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Pith citing papers

Observation 4b8ffbcf-d476-4748-9e42-aa280c02b9c4 · inbound