Paper Citation Record · LEDGER

Distilling foundation models for robust and efficient models in digital pathology

As of 18 August 2026, this Paper Citation Record lists 27 of 27 outbound references and 5 inbound Pith citation observations for arXiv:2501.16239.

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

pith.paper-citation-record.v1
2501.16239 v3

Coverage vector

measured 27 of 27 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T13:40:24.405750Z

measured 32 of 32 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T15:31:20.428942Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T07:26:45.465086Z

Reference resolution

27 of 27 outbound references displayed

verified exact3
verified fuzzy2
unresolved18
parse uncertain0
malformed identifier4
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation e798c7cf-87da-4119-9e2d-34a87c6df7f8 · outbound

This paper cites an unresolved cited work.

Distilling foundation models for robust and efficient models in digital pathology Unresolved cited work

Reference 1

Resolution: unresolved; raw_fallback, observed 2026-08-10T13:40:24.991269Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 3d722f77-96ce-4752-be2b-61a9a92d00e3 · outbound

This paper cites Emerging Properties in Self-Supervised Vision Transformers.

Distilling foundation models for robust and efficient models in digital pathology Emerging Properties in Self-Supervised Vision Transformers

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-10T13:40:24.292588Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 80141eaf-beb5-4331-80d4-b6f558546672 · outbound

This paper cites Nature Medicine 30(3), 850–862 (Mar 2024).

Distilling foundation models for robust and efficient models in digital pathology Nature Medicine 30(3), 850–862 (Mar 2024)

Reference 3

Resolution: malformed identifier; raw_fallback, observed 2026-08-10T13:40:24.977789Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation af32eb69-cb73-45ab-a263-39945d84edb7 · outbound

This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

Distilling foundation models for robust and efficient models in digital pathology An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-10T13:40:24.302051Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation efc92c3d-85b5-47e8-94b9-48ed67dcdc9a · outbound

This paper cites A Simple Recipe for Competitive Low-compute Self supervised Vision Models.

Distilling foundation models for robust and efficient models in digital pathology A Simple Recipe for Competitive Low-compute Self supervised Vision Models

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-10T13:40:24.306502Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 13cb9ee6-6109-423f-849f-210af5f99c4d · outbound

This paper cites 2023.07.21.23292757 (Jul 2023).

Distilling foundation models for robust and efficient models in digital pathology 2023.07.21.23292757 (Jul 2023)

Reference 6

Resolution: malformed identifier; raw_fallback, observed 2026-08-10T13:40:24.963399Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 0def1c6e-c5a9-4185-a5a2-f64d60fe5758 · outbound

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

Distilling foundation models for robust and efficient models in digital pathology Phikon-v2, A large and public feature extractor for biomarker prediction

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-10T13:40:24.317128Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b0b9e1a2-3491-43ab-88f8-8b0e463ad81b · outbound

This paper cites an unresolved cited work.

Distilling foundation models for robust and efficient models in digital pathology Unresolved cited work

Reference 8

Resolution: unresolved; raw_fallback, observed 2026-08-10T13:40:24.947368Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 6ae38545-4e94-46fe-b926-07e573ce8f69 · outbound

This paper cites Medical Imaging with Deep Learning (2024).

Distilling foundation models for robust and efficient models in digital pathology Medical Imaging with Deep Learning (2024)

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T13:40:24.932608Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation dbaec885-7812-4b3d-ba7e-c3c29572c476 · outbound

This paper cites Distilling the Knowledge in a Neural Network.

Distilling foundation models for robust and efficient models in digital pathology Distilling the Knowledge in a Neural Network

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-10T13:40:24.331148Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2cb4f65e-24a4-44ca-8cb3-8b75c90e919a · outbound

This paper cites Nature Medicine 29(9), 2307–2316 (2023).

Distilling foundation models for robust and efficient models in digital pathology Nature Medicine 29(9), 2307–2316 (2023)

Reference 11

Resolution: malformed identifier; raw_fallback, observed 2026-08-10T13:40:24.918208Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 8f617837-58fc-4948-b730-141b08818319 · outbound

This paper cites HEST-1k: A Dataset for Spatial Transcriptomics and Histology Image Analysis.

Distilling foundation models for robust and efficient models in digital pathology HEST-1k: A Dataset for Spatial Transcriptomics and Histology Image Analysis

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-10T13:40:24.340306Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fcb71a92-504d-4bec-ab37-2b54433850de · outbound

This paper cites an unresolved cited work.

Distilling foundation models for robust and efficient models in digital pathology Unresolved cited work

Reference 13

Resolution: unresolved; raw_fallback, observed 2026-08-10T13:40:24.904015Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 2b446f5c-758b-45eb-b1a0-6d9e28bb3f4a · outbound

This paper cites IEEE Trans Med Imaging 29(1), 196–205 (Nov 2009).

Distilling foundation models for robust and efficient models in digital pathology IEEE Trans Med Imaging 29(1), 196–205 (Nov 2009)

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T13:40:24.889842Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation d6d42b44-0be5-47a9-aada-fdcff3983aa6 · outbound

This paper cites Benchmarking Pathology Foundation Models: Adaptation Strategies and Scenarios.

Distilling foundation models for robust and efficient models in digital pathology Benchmarking Pathology Foundation Models: Adaptation Strategies and Scenarios

Reference 15

Resolution: verified exact; local_arxiv, observed 2026-08-10T13:40:24.676052Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 9f043557-73b7-43aa-8161-4cd6951365a2 · outbound

This paper cites Nature Medicine 30(3), 863–874 (2024).https://doi.org/10.1038/s41591-024-02856-4.

Distilling foundation models for robust and efficient models in digital pathology Nature Medicine 30(3), 863–874 (2024).https://doi.org/10.1038/s41591-024-02856-4

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-10T13:40:24.357729Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 58d29b4e-482d-49e5-92c3-e0677e81fb27 · outbound

This paper cites Towards A Generalizable Pathology Foundation Model via Unified Knowledge Distillation.

Distilling foundation models for robust and efficient models in digital pathology Towards A Generalizable Pathology Foundation Model via Unified Knowledge Distillation

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-10T13:40:24.361769Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a5a8ad56-889d-47b6-9dfe-29cdd5ef1098 · outbound

This paper cites an unresolved cited work.

Distilling foundation models for robust and efficient models in digital pathology Unresolved cited work

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-10T13:40:24.366094Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b96805ce-0651-400c-986a-465c19d93223 · outbound

This paper cites Scientific Data11(1), 330 (Apr 2024).https://doi.org/10.1038/s41597-024-03122-5.

Distilling foundation models for robust and efficient models in digital pathology Scientific Data11(1), 330 (Apr 2024).https://doi.org/10.1038/s41597-024-03122-5

Reference 19

Resolution: verified exact; doi, observed 2026-08-10T13:40:24.618406Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation c95e07a4-c485-40d8-9e63-374816995b34 · outbound

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

Distilling foundation models for robust and efficient models in digital pathology DINOv2: Learning Robust Visual Features without Supervision

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-10T13:40:24.374793Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 06e42205-6fa2-4951-b0a1-f3238c9e48f7 · outbound

This paper cites an unresolved cited work.

Distilling foundation models for robust and efficient models in digital pathology Unresolved cited work

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-10T13:40:24.379514Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2f1d078f-12dc-4f1b-8d06-ed8461933d2a · outbound

This paper cites Na- ture Reviews Bioengineering 1(12), 930–949 (2023).

Distilling foundation models for robust and efficient models in digital pathology Na- ture Reviews Bioengineering 1(12), 930–949 (2023)

Reference 22

Resolution: malformed identifier; raw_fallback, observed 2026-08-10T13:40:24.865565Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 77253049-eef2-4b04-88d7-7624348fbdfc · outbound

This paper cites 48550/ARXIV.2311.11772.

Distilling foundation models for robust and efficient models in digital pathology 48550/ARXIV.2311.11772

Reference 23

Resolution: verified exact; doi, observed 2026-08-10T13:40:24.584775Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation d16acf49-a2d6-4d3a-9364-3faee7bf292e · outbound

This paper cites Nature630(8015), 181–188 (Jun 2024).

Distilling foundation models for robust and efficient models in digital pathology Nature630(8015), 181–188 (Jun 2024)

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-10T13:40:24.392453Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7bc83f9c-1dbd-4381-9bcc-c2871371bb5f · outbound

This paper cites an unresolved cited work.

Distilling foundation models for robust and efficient models in digital pathology Unresolved cited work

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-10T13:40:24.396838Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6016a034-5c71-44f4-88dd-bb9fd9f6ceb2 · outbound

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

Distilling foundation models for robust and efficient models in digital pathology iBOT: Image BERT Pre-Training with Online Tokenizer

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-10T13:40:24.401146Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ebe6150f-909d-4571-b867-a6888b7022f2 · outbound

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

Distilling foundation models for robust and efficient models in digital pathology Virchow2: Scaling Self-Supervised Mixed Magnification Models in Pathology

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-10T13:40:24.405750Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation c0dc232e-9d3d-49b9-9e01-6ffbf12aff92 · inbound

Towards Robust Foundation Models for Digital Pathology cites this paper.

Towards Robust Foundation Models for Digital Pathology Distilling foundation models for robust and efficient models in digital pathology

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-06T15:11:14.704531Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e217e4c7-1269-4747-bff1-6bf113a593f5 · inbound

MOOZY: A Patient-First Foundation Model for Computational Pathology cites this paper.

MOOZY: A Patient-First Foundation Model for Computational Pathology Distilling foundation models for robust and efficient models in digital pathology

Reference 23

Resolution: unresolved; no resolver link, observed 2026-07-13T17:15:51.142086Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f0677c4e-3a97-4a87-869f-330cb63798f5 · inbound

Do Foundation Models See Biology? Evaluating Attention Coherence with Spatial Transcriptomics in Glioblastoma cites this paper.

Do Foundation Models See Biology? Evaluating Attention Coherence with Spatial Transcriptomics in Glioblastoma Distilling foundation models for robust and efficient models in digital pathology

Reference 5

Resolution: verified exact; arxiv_id, observed 2026-07-02T07:26:45.466613Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 02ed4d44-3261-47bf-a23a-fcb77fc21c80 · inbound

Robustifying pathology foundation models via fine-tuning cites this paper.

Robustifying pathology foundation models via fine-tuning Distilling foundation models for robust and efficient models in digital pathology

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-15T15:31:20.428942Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 559748c8-0378-46e0-b523-a0d3d70dd57a · inbound

Bag-of-Visual-Words for Spatial Mapping of Lung Adenocarcinoma Growth Patterns cites this paper.

Bag-of-Visual-Words for Spatial Mapping of Lung Adenocarcinoma Growth Patterns Distilling foundation models for robust and efficient models in digital pathology

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-06T06:05:44.605457Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.