Paper Citation Record · LEDGER

Divide and Conquer: A Large-Scale Dataset and Model for Left-Right Breast MRI Segmentation

As of 14 August 2026, this Paper Citation Record lists 13 of 13 outbound references and 0 inbound Pith citation observations for arXiv:2507.13830.

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

pith.paper-citation-record.v1
2507.13830 v1

Coverage vector

measured 13 of 13 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T16:19:39.167431Z

measured 13 of 13 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+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

13 of 13 outbound references displayed

verified exact0
verified fuzzy10
unresolved3
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 65358f4b-6c8c-44cb-adaa-c3e17b610e78 · outbound

This paper cites Weakly supervised 3d deep learning for breast cancer classification and localization of the lesions in mr images,.

Divide and Conquer: A Large-Scale Dataset and Model for Left-Right Breast MRI Segmentation Weakly supervised 3d deep learning for breast cancer classification and localization of the lesions in mr images,

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-06T16:19:39.080620Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7897fff9-8a6a-4519-b12a-cec7b4535aef · outbound

This paper cites Classification of breast cancer in mri with multimodal fusion,.

Divide and Conquer: A Large-Scale Dataset and Model for Left-Right Breast MRI Segmentation Classification of breast cancer in mri with multimodal fusion,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:19:39.543843Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation b868894a-9760-4b94-a601-2720c4e7ecfe · outbound

This paper cites Le- sionlocator: Zero-shot universal tumor segmentation and tracking in 3d whole-body imaging,.

Divide and Conquer: A Large-Scale Dataset and Model for Left-Right Breast MRI Segmentation Le- sionlocator: Zero-shot universal tumor segmentation and tracking in 3d whole-body imaging,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:19:39.520578Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation b578442f-41be-4353-8981-57d22b0aa50e · outbound

This paper cites Large language model with region-guided referring and grounding for ct report generation,.

Divide and Conquer: A Large-Scale Dataset and Model for Left-Right Breast MRI Segmentation Large language model with region-guided referring and grounding for ct report generation,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:19:39.499005Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation aec2388b-f805-4d75-bc59-c0a4e0e1bd89 · outbound

This paper cites Longitudinal segmentation of ms lesions via temporal difference weighting,.

Divide and Conquer: A Large-Scale Dataset and Model for Left-Right Breast MRI Segmentation Longitudinal segmentation of ms lesions via temporal difference weighting,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:19:39.474787Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation ff22e04d-d376-400d-9bc2-f674c09a313b · outbound

This paper cites How well do supervised 3d models trans- fer to medical imaging tasks?.

Divide and Conquer: A Large-Scale Dataset and Model for Left-Right Breast MRI Segmentation How well do supervised 3d models trans- fer to medical imaging tasks?

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:19:39.448316Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 8e2b1b88-49b5-47db-9767-3cd81627f2c2 · outbound

This paper cites Dynamic contrast- enhanced magnetic resonance images of breast cancer patients with tumor locations [data set],.

Divide and Conquer: A Large-Scale Dataset and Model for Left-Right Breast MRI Segmentation Dynamic contrast- enhanced magnetic resonance images of breast cancer patients with tumor locations [data set],

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:19:39.414162Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation d9f9cb33-3485-49a4-a63c-2edd24c9df8c · outbound

This paper cites A large- scale multicenter breast cancer dce-mri benchmark dataset with expert segmentations,.

Divide and Conquer: A Large-Scale Dataset and Model for Left-Right Breast MRI Segmentation A large- scale multicenter breast cancer dce-mri benchmark dataset with expert segmentations,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:19:39.377069Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation f161d465-43cb-4120-8ae7-9ca2b1ce6f89 · outbound

This paper cites nnu-net: a self-configuring method for deep learning-based biomedical image segmentation,.

Divide and Conquer: A Large-Scale Dataset and Model for Left-Right Breast MRI Segmentation nnu-net: a self-configuring method for deep learning-based biomedical image segmentation,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:19:39.350247Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 68acf37b-1714-46a8-befc-3a8d4bc6c9e6 · outbound

This paper cites Standard and delayed contrast-enhanced mri of malignant and benign breast lesions with histological and clinical supporting data (advanced-mri- breast-lesions) (version 2),.

Divide and Conquer: A Large-Scale Dataset and Model for Left-Right Breast MRI Segmentation Standard and delayed contrast-enhanced mri of malignant and benign breast lesions with histological and clinical supporting data (advanced-mri- breast-lesions) (version 2),

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-06T16:19:39.148611Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4bb0839f-51ee-49c7-92f8-568f135d8c37 · outbound

This paper cites Abbreviated breast mri and digital tomosyn- thesis mammography in screening women with dense breasts (ea1141),.

Divide and Conquer: A Large-Scale Dataset and Model for Left-Right Breast MRI Segmentation Abbreviated breast mri and digital tomosyn- thesis mammography in screening women with dense breasts (ea1141),

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:19:39.321761Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 352b0496-ae26-42e7-b974-91752bbe1ce9 · outbound

This paper cites Enhancing deep learning methods for brain metastasis detection through cross-technique annotations on space mri,.

Divide and Conquer: A Large-Scale Dataset and Model for Left-Right Breast MRI Segmentation Enhancing deep learning methods for brain metastasis detection through cross-technique annotations on space mri,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:19:39.296621Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation f1b0e9d2-78c3-402e-84a2-7df5d42950e8 · outbound

This paper cites nnInteractive: Redefining 3D Promptable Segmentation.

Divide and Conquer: A Large-Scale Dataset and Model for Left-Right Breast MRI Segmentation nnInteractive: Redefining 3D Promptable Segmentation

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-06T16:19:39.167431Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.