Paper Citation Record · LEDGER

Harnessing Adversarial Distillation to Customise Debiased, Disease-Specific Pathology Foundation Models for Breast Cancer

As of 7 August 2026, this Paper Citation Record lists 28 of 28 outbound references and 0 inbound Pith citation observations for arXiv:2608.01356.

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

pith.paper-citation-record.v1
2608.01356 v1

Coverage vector

measured 28 of 28 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T00:21:09.862888Z

measured 28 of 28 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

28 of 28 outbound references displayed

verified exact1
verified fuzzy11
unresolved16
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation e18e7b51-8212-4126-83d2-0373b9788377 · outbound

This paper cites NPJ Precision Oncology8(1), 151 (2024).

Harnessing Adversarial Distillation to Customise Debiased, Disease-Specific Pathology Foundation Models for Breast Cancer NPJ Precision Oncology8(1), 151 (2024)

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:21:10.092414Z

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 ce01b174-681c-4b98-88e2-d4842d0e6d34 · outbound

This paper cites Database2022, baac093 (2022).

Harnessing Adversarial Distillation to Customise Debiased, Disease-Specific Pathology Foundation Models for Breast Cancer Database2022, baac093 (2022)

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:21:10.084452Z

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 d57b25d2-642b-4c1f-ad36-3d7ed99f3f93 · outbound

This paper cites The Cancer Imaging Archive (2019), dOI: 10.7937/TCIA.2019.3XBN2JCC.

Harnessing Adversarial Distillation to Customise Debiased, Disease-Specific Pathology Foundation Models for Breast Cancer The Cancer Imaging Archive (2019), dOI: 10.7937/TCIA.2019.3XBN2JCC

Reference 3

Resolution: verified exact; doi, observed 2026-08-06T00:21:09.897362Z

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 566f7faa-40b2-4aef-891b-ca8c6337829f · outbound

This paper cites Nature medicine25(8), 1301–1309 (2019).

Harnessing Adversarial Distillation to Customise Debiased, Disease-Specific Pathology Foundation Models for Breast Cancer Nature medicine25(8), 1301–1309 (2019)

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-06T00:21:09.799445Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 167a9b37-7eb0-4e31-a9df-c9eaaf86fe33 · outbound

This paper cites In: Proceedings of the IEEE/CVF international conference on computer vision.

Harnessing Adversarial Distillation to Customise Debiased, Disease-Specific Pathology Foundation Models for Breast Cancer In: Proceedings of the IEEE/CVF international conference on computer vision

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-06T00:21:09.802257Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8afbbc25-21b9-4707-aca8-83c037b4de16 · outbound

This paper cites Nature Medicine (2024).

Harnessing Adversarial Distillation to Customise Debiased, Disease-Specific Pathology Foundation Models for Breast Cancer Nature Medicine (2024)

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:21:10.065726Z

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 637546b9-5f09-4133-99d1-8c2218e66949 · outbound

This paper cites Nature medicine24(10), 1559–1567 (2018).

Harnessing Adversarial Distillation to Customise Debiased, Disease-Specific Pathology Foundation Models for Breast Cancer Nature medicine24(10), 1559–1567 (2018)

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:21:10.057473Z

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 08194c5e-aa7d-4f82-9051-c5e009176764 · outbound

This paper cites Modern Pathology35(1), 44–51 (2022) 10 Zhiwei Chen, Yang Hu and Yuxiang Xiao et al.

Harnessing Adversarial Distillation to Customise Debiased, Disease-Specific Pathology Foundation Models for Breast Cancer Modern Pathology35(1), 44–51 (2022) 10 Zhiwei Chen, Yang Hu and Yuxiang Xiao et al

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:21:10.049507Z

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 7e0e141c-c83d-4b5e-9e6e-dc0e42e1f06f · outbound

This paper cites The Cancer Imaging Archive (2022), dOI: 10.7937/E65C-AM96.

Harnessing Adversarial Distillation to Customise Debiased, Disease-Specific Pathology Foundation Models for Breast Cancer The Cancer Imaging Archive (2022), dOI: 10.7937/E65C-AM96

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-06T00:21:09.813311Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 07565e84-aa56-4497-b29b-c4ad16a72c06 · outbound

This paper cites In: Medical Image Computing and Computer- Assisted Intervention – MICCAI 2025.

Harnessing Adversarial Distillation to Customise Debiased, Disease-Specific Pathology Foundation Models for Breast Cancer In: Medical Image Computing and Computer- Assisted Intervention – MICCAI 2025

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:21:10.041536Z

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 69ef1aca-09fd-4d92-8777-2368850a966c · outbound

This paper cites Phikon-v2, A large and public feature extractor for biomarker prediction.

Harnessing Adversarial Distillation to Customise Debiased, Disease-Specific Pathology Foundation Models for Breast Cancer Phikon-v2, A large and public feature extractor for biomarker prediction

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-06T00:21:09.818540Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e6038dd8-9951-4459-aadb-28665ae4f110 · outbound

This paper cites Journal of machine learning research17(59), 1–35 (2016).

Harnessing Adversarial Distillation to Customise Debiased, Disease-Specific Pathology Foundation Models for Breast Cancer Journal of machine learning research17(59), 1–35 (2016)

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-06T00:21:09.821158Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5fefa09d-b73d-46d1-b790-41d4b9a5cc0c · outbound

This paper cites Nature communications12(1), 4423 (2021).

Harnessing Adversarial Distillation to Customise Debiased, Disease-Specific Pathology Foundation Models for Breast Cancer Nature communications12(1), 4423 (2021)

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-06T00:21:09.823783Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7845b6de-47c7-4c7b-9555-38cbee777fc3 · outbound

This paper cites In: International conference on machine learning.

Harnessing Adversarial Distillation to Customise Debiased, Disease-Specific Pathology Foundation Models for Breast Cancer In: International conference on machine learning

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-06T00:21:09.826382Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 73a90adc-658c-4230-b345-46dc05b1c36e · outbound

This paper cites ACM Computing Surveys57(11), 1–37 (2025).

Harnessing Adversarial Distillation to Customise Debiased, Disease-Specific Pathology Foundation Models for Breast Cancer ACM Computing Surveys57(11), 1–37 (2025)

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:21:10.015676Z

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 77b35cc2-3822-40fd-98fd-e4398766b9ee · outbound

This paper cites Advances in neural information processing systems33, 18661–18673 (2020).

Harnessing Adversarial Distillation to Customise Debiased, Disease-Specific Pathology Foundation Models for Breast Cancer Advances in neural information processing systems33, 18661–18673 (2020)

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-06T00:21:09.830950Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c02d4a99-0e4d-4e9b-abf5-f6c59c034c02 · outbound

This paper cites Do Histopathological Foundation Models Eliminate Batch Effects? A Comparative Study.

Harnessing Adversarial Distillation to Customise Debiased, Disease-Specific Pathology Foundation Models for Breast Cancer Do Histopathological Foundation Models Eliminate Batch Effects? A Comparative Study

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-06T00:21:09.833697Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fda15408-954f-4076-9e93-15ac405fe61f · outbound

This paper cites A Survey on Computational Pathology Foundation Models: Datasets, Adaptation Strategies, and Evaluation Tasks.

Harnessing Adversarial Distillation to Customise Debiased, Disease-Specific Pathology Foundation Models for Breast Cancer A Survey on Computational Pathology Foundation Models: Datasets, Adaptation Strategies, and Evaluation Tasks

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-06T00:21:09.836589Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2b11a145-db66-431a-9020-d84935e7adb8 · outbound

This paper cites In: 2020 international joint conference on neural networks (IJCNN).

Harnessing Adversarial Distillation to Customise Debiased, Disease-Specific Pathology Foundation Models for Breast Cancer In: 2020 international joint conference on neural networks (IJCNN)

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-06T00:21:09.839411Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 970b96c1-bb3b-452e-a61c-85e858ac7e7f · outbound

This paper cites DINOv2: Learning Robust Visual Features without Supervision.

Harnessing Adversarial Distillation to Customise Debiased, Disease-Specific Pathology Foundation Models for Breast Cancer DINOv2: Learning Robust Visual Features without Supervision

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-06T00:21:09.841870Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b09fdd29-5fc5-4057-83f3-df4889f5b376 · outbound

This paper cites IEEE transactions on biomedical engineering61(5), 1400–1411 (2014).

Harnessing Adversarial Distillation to Customise Debiased, Disease-Specific Pathology Foundation Models for Breast Cancer IEEE transactions on biomedical engineering61(5), 1400–1411 (2014)

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:21:09.996595Z

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 8dcd8df9-2b63-49c7-9f02-209f3d3e91c9 · outbound

This paper cites Nature medicine30(10), 2924–2935 (2024).

Harnessing Adversarial Distillation to Customise Debiased, Disease-Specific Pathology Foundation Models for Breast Cancer Nature medicine30(10), 2924–2935 (2024)

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:21:09.988406Z

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 63d6a0a0-0f73-4541-9351-53ffbea64172 · outbound

This paper cites A Survey of Pathology Foundation Model: Progress and Future Directions.

Harnessing Adversarial Distillation to Customise Debiased, Disease-Specific Pathology Foundation Models for Breast Cancer A Survey of Pathology Foundation Model: Progress and Future Directions

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-06T00:21:09.850025Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a60b01ad-d6de-46c9-b4c7-bb93ce811d98 · outbound

This paper cites Nature630(8015), 181–188 (2024) Adversarial Distillation for Debiased Breast Cancer Foundation Models 11.

Harnessing Adversarial Distillation to Customise Debiased, Disease-Specific Pathology Foundation Models for Breast Cancer Nature630(8015), 181–188 (2024) Adversarial Distillation for Debiased Breast Cancer Foundation Models 11

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:21:09.979894Z

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 d86f1cc9-8b75-41e7-9526-710dd55cf92e · outbound

This paper cites Nature Communications (2025).

Harnessing Adversarial Distillation to Customise Debiased, Disease-Specific Pathology Foundation Models for Breast Cancer Nature Communications (2025)

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:21:09.970834Z

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 722a72da-0bfc-48a6-b38d-182f1bba8ce1 · outbound

This paper cites Accelerating Data Processing and Benchmarking of AI Models for Pathology.

Harnessing Adversarial Distillation to Customise Debiased, Disease-Specific Pathology Foundation Models for Breast Cancer Accelerating Data Processing and Benchmarking of AI Models for Pathology

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-06T00:21:09.857488Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b864e253-3e15-4b29-ba0b-8b35dab9a3f5 · outbound

This paper cites iBOT: Image BERT Pre-Training with Online Tokenizer.

Harnessing Adversarial Distillation to Customise Debiased, Disease-Specific Pathology Foundation Models for Breast Cancer iBOT: Image BERT Pre-Training with Online Tokenizer

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-06T00:21:09.860336Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2a8ecefc-9c35-4187-9fea-61864b1497a7 · outbound

This paper cites Virchow2: Scaling Self-Supervised Mixed Magnification Models in Pathology.

Harnessing Adversarial Distillation to Customise Debiased, Disease-Specific Pathology Foundation Models for Breast Cancer Virchow2: Scaling Self-Supervised Mixed Magnification Models in Pathology

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-06T00:21:09.862888Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.