Paper Citation Record · LEDGER

Exploring visual language models as a powerful tool in the diagnosis of Ewing Sarcoma

As of 22 August 2026, this Paper Citation Record lists 21 of 21 outbound references and 0 inbound Pith citation observations for arXiv:2501.08042.

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

pith.paper-citation-record.v1
2501.08042 v1

Coverage vector

measured 21 of 21 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T20:33:30.327946Z

measured 21 of 21 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+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

21 of 21 outbound references displayed

verified exact0
verified fuzzy19
unresolved2
parse uncertain0
malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation fd10bd31-0ed4-4d82-a703-4a5d687e06e0 · outbound

This paper cites The epidemiology of sarcoma,.

Exploring visual language models as a powerful tool in the diagnosis of Ewing Sarcoma The epidemiology of sarcoma,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:33:32.868040Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation c57e1e87-64aa-46e0-a3ca-65b9a5db92d1 · outbound

This paper cites Ewing tumour: incidence, prognosis and treatment options,.

Exploring visual language models as a powerful tool in the diagnosis of Ewing Sarcoma Ewing tumour: incidence, prognosis and treatment options,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:33:32.718706Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation df95a33d-a38b-4577-9218-17850c56266f · outbound

This paper cites Changes in incidence and survival of ewing sarcoma patients over the past 3 decades: Surveillance epidemiology and end results data,.

Exploring visual language models as a powerful tool in the diagnosis of Ewing Sarcoma Changes in incidence and survival of ewing sarcoma patients over the past 3 decades: Surveillance epidemiology and end results data,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:33:32.644269Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation d5bdc2b1-9452-43dc-aead-2af7955971d5 · outbound

This paper cites Two-phase deep learning algorithm for detection and differentiation of ewing sarcoma and acute osteomyeli- tis in paediatric radiographs,.

Exploring visual language models as a powerful tool in the diagnosis of Ewing Sarcoma Two-phase deep learning algorithm for detection and differentiation of ewing sarcoma and acute osteomyeli- tis in paediatric radiographs,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:33:32.421318Z

Source-reported events for the cited work

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Observation 04e59038-a08a-456a-83c0-3fb681e77371 · outbound

This paper cites Machine learning for rhabdomyosarcoma histopathology,.

Exploring visual language models as a powerful tool in the diagnosis of Ewing Sarcoma Machine learning for rhabdomyosarcoma histopathology,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:33:32.208013Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation d9c5b28a-6726-4604-aaa6-13e0f54371e8 · outbound

This paper cites Deep learning of rhab- domyosarcoma pathology images for classification and survival outcome predic- tion,.

Exploring visual language models as a powerful tool in the diagnosis of Ewing Sarcoma Deep learning of rhab- domyosarcoma pathology images for classification and survival outcome predic- tion,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:33:32.026445Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 19b8dda7-b755-405f-88b4-841baf701bd3 · outbound

This paper cites Predicting molecular subtype and survival of rhabdomyosarcoma patients using deep learning of h&e images: A report from the children’s oncology group,.

Exploring visual language models as a powerful tool in the diagnosis of Ewing Sarcoma Predicting molecular subtype and survival of rhabdomyosarcoma patients using deep learning of h&e images: A report from the children’s oncology group,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:33:31.879696Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 608d949f-8a86-4367-bb97-82ac813fea75 · outbound

This paper cites Deep learning based automated tool for cancer diagnosis from bone histopathology images,.

Exploring visual language models as a powerful tool in the diagnosis of Ewing Sarcoma Deep learning based automated tool for cancer diagnosis from bone histopathology images,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:33:31.667901Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 607eb0a8-c81d-48e1-b039-f98ad4f24e30 · outbound

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

Exploring visual language models as a powerful tool in the diagnosis of Ewing Sarcoma Learning transferable visual models from natural language supervision,

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-10T20:33:30.270282Z

Source-reported events for the cited work

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Observation ee275f95-8aa9-4d7e-b00b-681f07817a89 · outbound

This paper cites Scaling up visual and vision-language representation learning with noisy text supervision,.

Exploring visual language models as a powerful tool in the diagnosis of Ewing Sarcoma Scaling up visual and vision-language representation learning with noisy text supervision,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:33:31.419876Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 7e7efc94-99f7-4668-a3d9-6da660f9480b · outbound

This paper cites Contrastive learning of medical visual representations from paired images and text,.

Exploring visual language models as a powerful tool in the diagnosis of Ewing Sarcoma Contrastive learning of medical visual representations from paired images and text,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:33:31.202587Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 202a98ee-7cd9-4713-8092-526015c0b6b6 · outbound

This paper cites Towards a Visual-Language Foundation Model for Computational Pathology.

Exploring visual language models as a powerful tool in the diagnosis of Ewing Sarcoma Towards a Visual-Language Foundation Model for Computational Pathology

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-10T20:33:30.286198Z

Source-reported events for the cited work

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Observation b1a387b8-b863-4d74-b773-af88f3f5abf4 · outbound

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

Exploring visual language models as a powerful tool in the diagnosis of Ewing Sarcoma A visual– language foundation model for pathology image analysis using medical twitter,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:33:31.183359Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation cd7c7772-da0b-4631-90af-b90d75f45fd0 · outbound

This paper cites Detection of breast cancer from whole slide histopathological images using deep multiple instance cnn,.

Exploring visual language models as a powerful tool in the diagnosis of Ewing Sarcoma Detection of breast cancer from whole slide histopathological images using deep multiple instance cnn,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:33:30.988530Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 7108031f-c388-4632-9cbe-ee42eecefaea · outbound

This paper cites Self-learning for weakly supervised gleason grading of local patterns,.

Exploring visual language models as a powerful tool in the diagnosis of Ewing Sarcoma Self-learning for weakly supervised gleason grading of local patterns,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:33:30.903495Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation a7e14ba2-59dc-47fc-897b-64486e3a54c3 · outbound

This paper cites An attention-based weakly supervised framework for spitzoid melanocytic lesion diagnosis in whole slide images,.

Exploring visual language models as a powerful tool in the diagnosis of Ewing Sarcoma An attention-based weakly supervised framework for spitzoid melanocytic lesion diagnosis in whole slide images,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:33:30.782582Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation e131413d-5dac-4f5a-a69d-168f021bc6ae · outbound

This paper cites Predicting lymph node metastasis using histopathological images based on multiple instance learning with deep graph convolution,.

Exploring visual language models as a powerful tool in the diagnosis of Ewing Sarcoma Predicting lymph node metastasis using histopathological images based on multiple instance learning with deep graph convolution,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:33:30.735897Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation c226f3f2-e8e9-4b44-ae50-c89b3ea7e621 · outbound

This paper cites Classification of volu- metric images using multi-instance learning and extreme value theorem,.

Exploring visual language models as a powerful tool in the diagnosis of Ewing Sarcoma Classification of volu- metric images using multi-instance learning and extreme value theorem,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:33:30.573821Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation dad05439-9e02-4444-98ab-db98c496a760 · outbound

This paper cites Attention-based deep multiple instance learning,.

Exploring visual language models as a powerful tool in the diagnosis of Ewing Sarcoma Attention-based deep multiple instance learning,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:33:30.555945Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 980ce400-b246-426f-87c4-d43873107587 · outbound

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

Exploring visual language models as a powerful tool in the diagnosis of Ewing Sarcoma Clinical- grade computational pathology using weakly supervised deep learning on whole slide images,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:33:30.537968Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation edd1f88b-13fa-4c62-9602-6a479a0f4d40 · outbound

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

Exploring visual language models as a powerful tool in the diagnosis of Ewing Sarcoma Transmil: Transformer based correlated multiple instance learning for whole slide image classification,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:33:30.383061Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Pith citing papers

No inbound Pith citation observations are available.