Paper Citation Record · LEDGER

Label-free Concept Based Multiple Instance Learning for Gigapixel Histopathology

As of 21 August 2026, this Paper Citation Record lists 45 of 45 outbound references and 1 inbound Pith citation observation for arXiv:2501.02922.

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

pith.paper-citation-record.v1
2501.02922 v1

Coverage vector

measured 45 of 45 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T22:05:32.642694Z

measured 46 of 46 standing notices

One-hop event checks from named stored sources.

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measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-28T02:31:34.975093Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T12:06:55.825145Z

Reference resolution

45 of 45 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation fe51ba97-39b8-402f-9e1c-7de83e09ad46 · outbound

This paper cites Sanity checks for saliency maps.

Label-free Concept Based Multiple Instance Learning for Gigapixel Histopathology Sanity checks for saliency maps

Reference 1

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Observation f0a84e1e-a8ae-4d85-bab0-05ad16bf685b · outbound

This paper cites Diagnostic assessment of deep learning algorithms for detec- tion of lymph node metastases in women with breast cancer.

Label-free Concept Based Multiple Instance Learning for Gigapixel Histopathology Diagnostic assessment of deep learning algorithms for detec- tion of lymph node metastases in women with breast cancer

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:05:33.371114Z

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Observation aeeb21b8-7668-485e-b59f-ff81a7c14977 · outbound

This paper cites B-cos networks: Align- ment is all we need for interpretability.

Label-free Concept Based Multiple Instance Learning for Gigapixel Histopathology B-cos networks: Align- ment is all we need for interpretability

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:05:33.356026Z

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Observation 0c3368d6-fce7-4f8f-8054-c1e2fe4250af · outbound

This paper cites Steiner, Hester van Boven, Robert Vink, Christina Hulsbergen van de Kaa, Jeroen van der Laak, Mahul B.

Label-free Concept Based Multiple Instance Learning for Gigapixel Histopathology Steiner, Hester van Boven, Robert Vink, Christina Hulsbergen van de Kaa, Jeroen van der Laak, Mahul B

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:05:33.338477Z

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Observation 1cba7ff5-5813-4a63-8a24-23394f08ec63 · outbound

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

Label-free Concept Based Multiple Instance Learning for Gigapixel Histopathology Clinical-grade com- putational pathology using weakly supervised deep learning on whole slide images

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:05:33.321994Z

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Observation 3c90e1eb-8946-4549-8713-d481ab66cbee · outbound

This paper cites This looks like that: deep learning for interpretable image recognition.

Label-free Concept Based Multiple Instance Learning for Gigapixel Histopathology This looks like that: deep learning for interpretable image recognition

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:05:33.302859Z

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Observation a2592131-ec88-4367-94e5-4b020153458e · outbound

This paper cites Differ- entiable patch selection for image recognition.

Label-free Concept Based Multiple Instance Learning for Gigapixel Histopathology Differ- entiable patch selection for image recognition

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:05:33.286568Z

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Observation 9fedbe0d-746e-407e-9027-3c72ebe58635 · outbound

This paper cites Clinically applicable deep learning for diagnosis and referral in retinal disease.

Label-free Concept Based Multiple Instance Learning for Gigapixel Histopathology Clinically applicable deep learning for diagnosis and referral in retinal disease

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:05:33.269958Z

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Observation fb70d1fd-b1b9-4854-926f-6074a9c9417b · outbound

This paper cites Inherently interpretable position-aware convolutional motif kernel networks for biological se- quencing data.

Label-free Concept Based Multiple Instance Learning for Gigapixel Histopathology Inherently interpretable position-aware convolutional motif kernel networks for biological se- quencing data

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:05:33.253461Z

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Observation fdc23aad-24f8-4872-971b-320b82b2422d · outbound

This paper cites Sparse activations for interpretable disease grading.

Label-free Concept Based Multiple Instance Learning for Gigapixel Histopathology Sparse activations for interpretable disease grading

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:05:33.234718Z

Source-reported events for the cited work

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Observation d3c39599-3aca-40d7-8427-64e62d7da823 · outbound

This paper cites Gigapixel end-to-end training using streaming and attention.

Label-free Concept Based Multiple Instance Learning for Gigapixel Histopathology Gigapixel end-to-end training using streaming and attention

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:05:33.216021Z

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Observation 27623064-ccd5-40d6-8ccc-ced4704d32d8 · outbound

This paper cites An update of the gleason grading system.

Label-free Concept Based Multiple Instance Learning for Gigapixel Histopathology An update of the gleason grading system

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:05:33.198866Z

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Observation 50ea5200-5b55-43eb-ab23-224fcd348651 · outbound

This paper cites an unresolved cited work.

Label-free Concept Based Multiple Instance Learning for Gigapixel Histopathology Unresolved cited work

Reference 13

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Observation d00adb6b-2632-43df-85c0-81fed6c50d7e · outbound

This paper cites Pannuke: an open pan-cancer histology dataset for nuclei instance segmentation and classification.

Label-free Concept Based Multiple Instance Learning for Gigapixel Histopathology Pannuke: an open pan-cancer histology dataset for nuclei instance segmentation and classification

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:05:33.166042Z

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Observation d7323091-b56c-4f3a-8b2a-66bb007abcdd · outbound

This paper cites Towards automatic concept-based explanations.

Label-free Concept Based Multiple Instance Learning for Gigapixel Histopathology Towards automatic concept-based explanations

Reference 15

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Observation 153316c0-83da-4b7b-ada9-6d45754b694f · outbound

This paper cites Hover-net: Simulta- neous segmentation and classification of nuclei in multi-tissue histology images.

Label-free Concept Based Multiple Instance Learning for Gigapixel Histopathology Hover-net: Simulta- neous segmentation and classification of nuclei in multi-tissue histology images

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:05:33.138591Z

Source-reported events for the cited work

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Observation 37a00117-8b60-4862-a887-e6617c6b973c · outbound

This paper cites Regression concept vectors for bidirectional explanations in histopathology.

Label-free Concept Based Multiple Instance Learning for Gigapixel Histopathology Regression concept vectors for bidirectional explanations in histopathology

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:05:33.120650Z

Source-reported events for the cited work

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Observation a530f87a-f3e7-478c-858f-0b775470af85 · outbound

This paper cites Explainable discovery of disease biomarkers: The case of ovarian cancer to illustrate the best practice in machine learning and shapley analysis.

Label-free Concept Based Multiple Instance Learning for Gigapixel Histopathology Explainable discovery of disease biomarkers: The case of ovarian cancer to illustrate the best practice in machine learning and shapley analysis

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:05:33.098726Z

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Observation f0794978-0e23-48b6-9482-a58a58bb0954 · outbound

This paper cites A visual–language foundation model for pathology image analysis using medical twitter.

Label-free Concept Based Multiple Instance Learning for Gigapixel Histopathology A visual–language foundation model for pathology image analysis using medical twitter

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:05:33.080932Z

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Observation 969e1709-1ffc-4c3f-8dcc-5eb165da49a8 · outbound

This paper cites Attention-based deep multiple instance learning.

Label-free Concept Based Multiple Instance Learning for Gigapixel Histopathology Attention-based deep multiple instance learning

Reference 20

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Observation 28257b32-e2d6-43f5-8360-b3b755e0cbe3 · outbound

This paper cites Additive mil: Intrinsically interpretable multiple instance learning for pathology.

Label-free Concept Based Multiple Instance Learning for Gigapixel Histopathology Additive mil: Intrinsically interpretable multiple instance learning for pathology

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:05:33.052561Z

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Observation 2824af7b-908d-4074-bc1e-c32f7cce8748 · outbound

This paper cites Si-mil: Taming deep mil for self-interpretability in gigapixel histopathology.

Label-free Concept Based Multiple Instance Learning for Gigapixel Histopathology Si-mil: Taming deep mil for self-interpretability in gigapixel histopathology

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:05:33.033237Z

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Observation f16bcedc-59ce-4d89-be1d-69baf44b63e4 · outbound

This paper cites Interpretability beyond feature attribution: Quan- titative testing with concept activation vectors (tcav).

Label-free Concept Based Multiple Instance Learning for Gigapixel Histopathology Interpretability beyond feature attribution: Quan- titative testing with concept activation vectors (tcav)

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:05:33.016717Z

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Observation c5730dfc-e5bf-4585-be16-4ea75ab522c4 · outbound

This paper cites Concept bottleneck models.

Label-free Concept Based Multiple Instance Learning for Gigapixel Histopathology Concept bottleneck models

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:05:33.000161Z

Source-reported events for the cited work

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Observation 997a6aad-a20f-4803-abfc-2c4cb4ae979d · outbound

This paper cites A multi-resolution model for histopathology image classification and localization with multiple instance learning.

Label-free Concept Based Multiple Instance Learning for Gigapixel Histopathology A multi-resolution model for histopathology image classification and localization with multiple instance learning

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:05:32.983998Z

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 3268c113-1149-4ed1-aa80-178ec502fe3c · outbound

This paper cites A visual-language foundation model for computational pathology.

Label-free Concept Based Multiple Instance Learning for Gigapixel Histopathology A visual-language foundation model for computational pathology

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:05:32.968486Z

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Observation 9a5d88b3-b6ca-442e-831e-90cea67c8064 · outbound

This paper cites Data-efficient and weakly supervised computational pathology on whole-slide images.

Label-free Concept Based Multiple Instance Learning for Gigapixel Histopathology Data-efficient and weakly supervised computational pathology on whole-slide images

Reference 27

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Observation d654f28e-7b17-4a3f-95be-cc768e0b9649 · outbound

This paper cites Label-Free Concept Bottleneck Models.

Label-free Concept Based Multiple Instance Learning for Gigapixel Histopathology Label-Free Concept Bottleneck Models

Reference 28

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Observation 9b4cbbe2-9064-4597-a6be-e988331484a9 · outbound

This paper cites Chatgpt: Optimizing language models for dialogue.

Label-free Concept Based Multiple Instance Learning for Gigapixel Histopathology Chatgpt: Optimizing language models for dialogue

Reference 29

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Observation db661009-d5b9-4ca6-ad3a-d5ab50951622 · outbound

This paper cites Predicting biochemical recurrence of prostate cancer with artificial intelligence.

Label-free Concept Based Multiple Instance Learning for Gigapixel Histopathology Predicting biochemical recurrence of prostate cancer with artificial intelligence

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:05:32.924991Z

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 4678fe69-8007-4797-ae81-db904d95418d · outbound

This paper cites Improving interpretability for computer-aided diag- nosis tools on whole slide imaging with multiple instance learning and gradient-based explanations.

Label-free Concept Based Multiple Instance Learning for Gigapixel Histopathology Improving interpretability for computer-aided diag- nosis tools on whole slide imaging with multiple instance learning and gradient-based explanations

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:05:32.908519Z

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Observation b6f6d91f-6b3a-46f8-82d6-242d52458c8a · outbound

This paper cites Learning transferable visual models from natural language supervision.

Label-free Concept Based Multiple Instance Learning for Gigapixel Histopathology Learning transferable visual models from natural language supervision

Reference 32

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Observation c0ed8587-5f76-4588-9df3-b6ba97af6685 · outbound

This paper cites Discover-then-Name: Task-Agnostic Concept Bottlenecks via Automated Concept Discovery.

Label-free Concept Based Multiple Instance Learning for Gigapixel Histopathology Discover-then-Name: Task-Agnostic Concept Bottlenecks via Automated Concept Discovery

Reference 33

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Observation 9ee4879b-40e3-44b7-b046-dc3353102b36 · outbound

This paper cites Silhouettes: a graphical aid to the interpretation and validation of cluster analysis.

Label-free Concept Based Multiple Instance Learning for Gigapixel Histopathology Silhouettes: a graphical aid to the interpretation and validation of cluster analysis

Reference 34

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Observation 56ee811a-3fd4-4a81-ab70-bb7d29927d16 · outbound

This paper cites Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead.

Label-free Concept Based Multiple Instance Learning for Gigapixel Histopathology Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-10T22:05:32.593485Z

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Observation 940b7066-a06e-4609-8a8b-f5f288ee4880 · outbound

This paper cites Transmil: Transformer based correlated multiple instance learning for whole slide image classification.

Label-free Concept Based Multiple Instance Learning for Gigapixel Histopathology Transmil: Transformer based correlated multiple instance learning for whole slide image classification

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-10T22:05:32.598305Z

Source-reported events for the cited work

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Observation bd6a6f7f-e781-4aec-b10b-0cdc9d6fa930 · outbound

This paper cites Deep neural network models for computational histopathology: A survey.

Label-free Concept Based Multiple Instance Learning for Gigapixel Histopathology Deep neural network models for computational histopathology: A survey

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-10T22:05:32.603149Z

Source-reported events for the cited work

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Observation fa15526b-fc15-4b92-838b-168d35430fd2 · outbound

This paper cites Inherently Interpretable Multi-Label Classification Using Class-Specific Counterfactuals.

Label-free Concept Based Multiple Instance Learning for Gigapixel Histopathology Inherently Interpretable Multi-Label Classification Using Class-Specific Counterfactuals

Reference 38

Resolution: verified exact; local_arxiv, observed 2026-08-10T22:05:32.708689Z

Source-reported events for the cited work

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Observation 5f8ffe8f-43cc-44ce-b01c-6ba67cfe5463 · outbound

This paper cites Differentiable zooming for multiple instance learning on whole-slide images.

Label-free Concept Based Multiple Instance Learning for Gigapixel Histopathology Differentiable zooming for multiple instance learning on whole-slide images

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:05:32.840933Z

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Observation 85e84bef-95d5-4cb2-ba70-d38a0c85c29c · outbound

This paper cites Visualizing data using t-sne.

Label-free Concept Based Multiple Instance Learning for Gigapixel Histopathology Visualizing data using t-sne

Reference 40

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Source-reported events for the cited work

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Observation 68079286-b9ae-4d21-85d4-39778afd624f · outbound

This paper cites Machine learning models for multiparametric glioma grading with quantitative result interpretations.

Label-free Concept Based Multiple Instance Learning for Gigapixel Histopathology Machine learning models for multiparametric glioma grading with quantitative result interpretations

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:05:32.812107Z

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Observation daebf0c8-063d-4569-b8e8-d538522561f3 · outbound

This paper cites Mprotonet: A case- based interpretable model for brain tumor classification with 3d multi- parametric magnetic resonance imaging.

Label-free Concept Based Multiple Instance Learning for Gigapixel Histopathology Mprotonet: A case- based interpretable model for brain tumor classification with 3d multi- parametric magnetic resonance imaging

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:05:32.795411Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation a9a5e65f-82ce-483e-96de-285d57b4969e · outbound

This paper cites Whole slide images based cancer survival predic- 11 tion using attention guided deep multiple instance learning networks.

Label-free Concept Based Multiple Instance Learning for Gigapixel Histopathology Whole slide images based cancer survival predic- 11 tion using attention guided deep multiple instance learning networks

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:05:32.776842Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation af288692-86de-4389-94de-9879648a1c20 · outbound

This paper cites Post-hoc Concept Bottleneck Models.

Label-free Concept Based Multiple Instance Learning for Gigapixel Histopathology Post-hoc Concept Bottleneck Models

Reference 44

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Source-reported events for the cited work

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Observation a8fd00b4-8471-4586-9086-4967fb5f0714 · outbound

This paper cites Top-down neural attention by excitation backprop.

Label-free Concept Based Multiple Instance Learning for Gigapixel Histopathology Top-down neural attention by excitation backprop

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:05:32.761010Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Pith citing papers

Observation dc843103-53d3-4cd8-9dd1-856b43390fe5 · inbound

Symb-xMIL: Symbolic Explanations for Multiple Instance Learning in Digital Pathology cites this paper.

Symb-xMIL: Symbolic Explanations for Multiple Instance Learning in Digital Pathology Label-free Concept Based Multiple Instance Learning for Gigapixel Histopathology

Reference 47

Resolution: verified exact; arxiv_id, observed 2026-07-02T12:06:55.826774Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.