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Paper Citation Record · LEDGER

Solutions for Mitotic Figure Detection and Atypical Classification in MIDOG 2025

As of 7 August 2026, this Paper Citation Record lists 31 of 31 outbound references and 0 inbound Pith citation observations for arXiv:2509.02597.

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

pith.paper-citation-record.v1
2509.02597 v1

Coverage vector

measured 31 of 31 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T14:04:48.470660Z

measured 31 of 31 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.

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measured 0 of 1 external citation measurements

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Source: cited_works

Reference resolution

31 of 31 outbound references displayed

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External citation measurements

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Outbound references

Observation 1ea11ffc-7272-44e8-a92d-faad9ebd4e46 · outbound

This paper cites Periphery-aware covid-19 diagnosis with contrastive representation enhancement.

Solutions for Mitotic Figure Detection and Atypical Classification in MIDOG 2025 Periphery-aware covid-19 diagnosis with contrastive representation enhancement

Reference 1

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Observation f4aaeb34-dabc-4bcd-bcf9-bae2e5f13412 · outbound

This paper cites Cross-field transformer for diabetic retinopathy grading on two-field fundus images.

Solutions for Mitotic Figure Detection and Atypical Classification in MIDOG 2025 Cross-field transformer for diabetic retinopathy grading on two-field fundus images

Reference 2

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Observation f8d55950-34de-4bd2-bc12-72e84eef84c2 · outbound

This paper cites Diabetic retinopathy grading with weakly-supervised lesion priors.

Solutions for Mitotic Figure Detection and Atypical Classification in MIDOG 2025 Diabetic retinopathy grading with weakly-supervised lesion priors

Reference 3

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Observation 2c9db953-63da-464e-a326-b83740ff6747 · outbound

This paper cites Self-eXplainable AI for Medical Image Analysis: A Survey and New Outlooks.

Solutions for Mitotic Figure Detection and Atypical Classification in MIDOG 2025 Self-eXplainable AI for Medical Image Analysis: A Survey and New Outlooks

Reference 4

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Observation 38999566-67f7-457a-bb29-32005959ae79 · outbound

This paper cites Concept-attention whitening for interpretable skin lesion diagnosis.

Solutions for Mitotic Figure Detection and Atypical Classification in MIDOG 2025 Concept-attention whitening for interpretable skin lesion diagnosis

Reference 5

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation c23dc1db-60d3-4486-9506-404c0dc02563 · outbound

This paper cites Qmix: Quality-aware learning with mixed noise for robust retinal disease diagnosis.

Solutions for Mitotic Figure Detection and Atypical Classification in MIDOG 2025 Qmix: Quality-aware learning with mixed noise for robust retinal disease diagnosis

Reference 6

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Source-reported events for the cited work

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Observation 5ebc6a07-b06c-4f87-8eb6-c0ca95c7ae94 · outbound

This paper cites Data-efficient histopathology image analysis with deformation representation learning.

Solutions for Mitotic Figure Detection and Atypical Classification in MIDOG 2025 Data-efficient histopathology image analysis with deformation representation learning

Reference 7

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Observation 5139590a-a025-4184-bb99-0701ae772204 · outbound

This paper cites Segment Anything in Pathology Images with Natural Language.

Solutions for Mitotic Figure Detection and Atypical Classification in MIDOG 2025 Segment Anything in Pathology Images with Natural Language

Reference 8

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Observation 4ecd0536-928e-4911-9421-588daba6ad74 · outbound

This paper cites Mitosis detection in breast cancer histology images with deep neural networks.

Solutions for Mitotic Figure Detection and Atypical Classification in MIDOG 2025 Mitosis detection in breast cancer histology images with deep neural networks

Reference 9

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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.

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Observation eb1a0ff1-5b03-432f-98c6-dae81d4e0f85 · outbound

This paper cites Mitosis detection in breast cancer histology images via deep cascaded networks.

Solutions for Mitotic Figure Detection and Atypical Classification in MIDOG 2025 Mitosis detection in breast cancer histology images via deep cascaded networks

Reference 10

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation 52c23d51-7bbc-4f26-8299-f65c20d76518 · outbound

This paper cites Mitosis detection techniques in h&e stained breast cancer pathological images: A comprehensive review.

Solutions for Mitotic Figure Detection and Atypical Classification in MIDOG 2025 Mitosis detection techniques in h&e stained breast cancer pathological images: A comprehensive review

Reference 11

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Observation a3457d39-7743-410a-9fb7-8f8238c0e6b5 · outbound

This paper cites A comprehensive multi-domain dataset for mitotic figure detection.

Solutions for Mitotic Figure Detection and Atypical Classification in MIDOG 2025 A comprehensive multi-domain dataset for mitotic figure detection

Reference 12

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Observation 59155ce4-db03-4e62-8467-67902041c48d · outbound

This paper cites A large-scale dataset for mitotic figure assessment on whole slide images of canine cutaneous mast cell tumor.

Solutions for Mitotic Figure Detection and Atypical Classification in MIDOG 2025 A large-scale dataset for mitotic figure assessment on whole slide images of canine cutaneous mast cell tumor

Reference 13

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Observation 8b53bafe-af9b-4547-bdde-803d8160dd00 · outbound

This paper cites A completely annotated whole slide image dataset of canine breast cancer to aid human breast cancer research.

Solutions for Mitotic Figure Detection and Atypical Classification in MIDOG 2025 A completely annotated whole slide image dataset of canine breast cancer to aid human breast cancer research

Reference 14

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Observation a52b2f04-b74d-4c39-a3a1-c34c70172c62 · outbound

This paper cites Fcos: Fully convolutional one-stage object detection.

Solutions for Mitotic Figure Detection and Atypical Classification in MIDOG 2025 Fcos: Fully convolutional one-stage object detection

Reference 15

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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.

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Observation 7f3e92ca-94ef-4fb1-b2e9-10a4903d6fcb · outbound

This paper cites Deep residual learning for image recognition.

Solutions for Mitotic Figure Detection and Atypical Classification in MIDOG 2025 Deep residual learning for image recognition

Reference 16

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Observation e8fd58f1-ae3e-4db2-aa71-d44195e7e360 · outbound

This paper cites A method for stochastic optimization.

Solutions for Mitotic Figure Detection and Atypical Classification in MIDOG 2025 A method for stochastic optimization

Reference 17

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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.

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Observation befdc4cf-157b-42e3-a013-71bac58bc951 · outbound

This paper cites A stochastic approximation method.

Solutions for Mitotic Figure Detection and Atypical Classification in MIDOG 2025 A stochastic approximation method

Reference 18

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Observation a78386d0-8657-410f-b02f-de7c8fab291b · outbound

This paper cites A dataset of atypical vs normal mitoses classification for midog - 2025, April 2025.

Solutions for Mitotic Figure Detection and Atypical Classification in MIDOG 2025 A dataset of atypical vs normal mitoses classification for midog - 2025, April 2025

Reference 19

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Observation 565224da-80a6-4230-9873-82732175733f · outbound

This paper cites His- tologic dataset of normal and atypical mitotic figures on human breast cancer (ami-br).

Solutions for Mitotic Figure Detection and Atypical Classification in MIDOG 2025 His- tologic dataset of normal and atypical mitotic figures on human breast cancer (ami-br)

Reference 20

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Observation a7f0d879-d201-45f1-9d3e-a3eb4a0485f0 · outbound

This paper cites Mitosis domain generalization in histopathology images -- The MIDOG challenge.

Solutions for Mitotic Figure Detection and Atypical Classification in MIDOG 2025 Mitosis domain generalization in histopathology images -- The MIDOG challenge

Reference 21

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Observation f40732f9-04ca-49e8-a030-efe16bd97fda · outbound

This paper cites Pre- dicting breast tumor proliferation from whole-slide images: the tupac16 challenge.

Solutions for Mitotic Figure Detection and Atypical Classification in MIDOG 2025 Pre- dicting breast tumor proliferation from whole-slide images: the tupac16 challenge

Reference 22

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Observation 47a4ce5d-f15d-4592-8d37-1df6d00fcfd0 · outbound

This paper cites An image is worth 16x16 words: Transformers for image recognition at scale.

Solutions for Mitotic Figure Detection and Atypical Classification in MIDOG 2025 An image is worth 16x16 words: Transformers for image recognition at scale

Reference 23

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Observation 550b35f3-3a00-4d36-a778-8c011a18a5d8 · outbound

This paper cites Densely connected convolutional networks.

Solutions for Mitotic Figure Detection and Atypical Classification in MIDOG 2025 Densely connected convolutional networks

Reference 24

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Observation 170ac82b-3301-4b21-b895-cc30c2cbf744 · outbound

This paper cites A convnet for the 2020s.

Solutions for Mitotic Figure Detection and Atypical Classification in MIDOG 2025 A convnet for the 2020s

Reference 25

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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.

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Observation 6b9247bf-e0c5-434f-9440-59e7e5ca0ed9 · outbound

This paper cites Cbam: Convolutional block attention module.

Solutions for Mitotic Figure Detection and Atypical Classification in MIDOG 2025 Cbam: Convolutional block attention module

Reference 26

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Observation d0c68664-58f2-4bb2-9042-fc148648f917 · outbound

This paper cites Efficientnet: Rethinking model scaling for convolutional neural networks.

Solutions for Mitotic Figure Detection and Atypical Classification in MIDOG 2025 Efficientnet: Rethinking model scaling for convolutional neural networks

Reference 27

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Observation 820ef211-9200-4c06-b9b9-24da5773f60c · outbound

This paper cites Cmc-cov19d: Contrastive mixup classification for covid-19 diagnosis.

Solutions for Mitotic Figure Detection and Atypical Classification in MIDOG 2025 Cmc-cov19d: Contrastive mixup classification for covid-19 diagnosis

Reference 28

Resolution
verified fuzzy
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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.

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Observation 1ca5fca9-499d-4b36-93a1-9185693558f6 · outbound

This paper cites Faster r-cnn: Towards real-time object detection with region proposal networks.

Solutions for Mitotic Figure Detection and Atypical Classification in MIDOG 2025 Faster r-cnn: Towards real-time object detection with region proposal networks

Reference 29

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raw_fallback, observed 2026-08-05T14:04:48.931295Z

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.

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Observation 843ffbe8-9b8a-4007-927d-cca7afef9b06 · outbound

This paper cites Focal loss for dense object detection.

Solutions for Mitotic Figure Detection and Atypical Classification in MIDOG 2025 Focal loss for dense object detection

Reference 30

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Observation 6a5b9edc-ad62-4e27-b42d-a60d86bd285e · outbound

This paper cites Aggregated resid- ual transformations for deep neural networks.

Solutions for Mitotic Figure Detection and Atypical Classification in MIDOG 2025 Aggregated resid- ual transformations for deep neural networks

Reference 31

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verified fuzzy
raw_fallback, observed 2026-08-05T14:04:48.750521Z

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.

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Pith citing papers

No inbound Pith citation observations are available.