Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-05T18:05:14.681760Z
Paper Citation Record · LEDGER
DyMorph-B2I: Dynamic and Morphology-Guided Binary-to-Instance Segmentation for Renal Pathology
As of 11 August 2026, this Paper Citation Record lists 21 of 21 outbound references and 1 inbound Pith citation observation for arXiv:2508.15208.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-06-29T13:52:32.362858Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-06-29T13:53:28.485850Z
Reference resolution
21 of 21 outbound references displayed
- verified exact1
- verified fuzzy20
- unresolved0
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation 1970f66f-65f6-4ad9-8708-dda162c95e6b · outbound
This paper cites Fully Convolutional Instance-Aware Semantic Segmentation ,.
DyMorph-B2I: Dynamic and Morphology-Guided Binary-to-Instance Segmentation for Renal Pathology Fully Convolutional Instance-Aware Semantic Segmentation ,
Reference 1
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-05T18:05:18.021341Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation dba296cd-81c7-4907-8743-0222f7a77a80 · outbound
This paper cites Cross-species data inte- gration for enhanced layer segmentation in kidney pathology,.
DyMorph-B2I: Dynamic and Morphology-Guided Binary-to-Instance Segmentation for Renal Pathology Cross-species data inte- gration for enhanced layer segmentation in kidney pathology,
Reference 2
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-05T18:05:17.849126Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation e4a1fc57-8b1f-478e-a65f-c91f6ad3c9ca · outbound
This paper cites Tshfna-examiner: A nuclei segmentation and cancer assessment framework for thyroid cytology image,.
DyMorph-B2I: Dynamic and Morphology-Guided Binary-to-Instance Segmentation for Renal Pathology Tshfna-examiner: A nuclei segmentation and cancer assessment framework for thyroid cytology image,
Reference 3
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-05T18:05:17.693484Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation a4a51cb8-82ca-4e7d-96bb-161b205ada82 · outbound
This paper cites Omni- seg: A scale-aware dynamic network for renal pathological image segmentation,.
DyMorph-B2I: Dynamic and Morphology-Guided Binary-to-Instance Segmentation for Renal Pathology Omni- seg: A scale-aware dynamic network for renal pathological image segmentation,
Reference 4
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-05T18:05:17.592418Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 6d17a087-d4be-4ab1-82ec-f982439308d7 · outbound
This paper cites Prpseg: Universal proposition learning for panoramic renal pathology segmentation,.
DyMorph-B2I: Dynamic and Morphology-Guided Binary-to-Instance Segmentation for Renal Pathology Prpseg: Universal proposition learning for panoramic renal pathology segmentation,
Reference 5
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-05T18:05:17.490629Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 90e329f0-1d54-4874-9563-cf7a01836429 · outbound
This paper cites Segment anything model (sam) for digital pathology: Assess zero-shot segmentation on whole slide imaging,.
DyMorph-B2I: Dynamic and Morphology-Guided Binary-to-Instance Segmentation for Renal Pathology Segment anything model (sam) for digital pathology: Assess zero-shot segmentation on whole slide imaging,
Reference 6
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-05T18:05:17.352737Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 3b322eed-c12e-42e7-9bd9-2f3c0cb8a045 · outbound
This paper cites Casc-ai: Consensus-aware self-corrective learning for cell segmentation with noisy labels,.
DyMorph-B2I: Dynamic and Morphology-Guided Binary-to-Instance Segmentation for Renal Pathology Casc-ai: Consensus-aware self-corrective learning for cell segmentation with noisy labels,
Reference 7
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-05T18:05:17.144257Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation bf8e171e-6968-4ea3-a74c-38ed36d1bbcc · outbound
This paper cites Democratizing pathological image segmentation with lay annotators via molecular-empowered learning,.
DyMorph-B2I: Dynamic and Morphology-Guided Binary-to-Instance Segmentation for Renal Pathology Democratizing pathological image segmentation with lay annotators via molecular-empowered learning,
Reference 8
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-05T18:05:16.985415Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation e2847545-d1f0-441e-a52e-2803740d67ba · outbound
This paper cites Development and evaluation of deep learning–based segmentation of histologic structures in the kidney cortex with multiple histologic stains,.
DyMorph-B2I: Dynamic and Morphology-Guided Binary-to-Instance Segmentation for Renal Pathology Development and evaluation of deep learning–based segmentation of histologic structures in the kidney cortex with multiple histologic stains,
Reference 9
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-05T18:05:16.850456Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation b3b7882b-5d05-4cff-97f9-7c3e310af85c · outbound
This paper cites Deep learning–based segmentation and quantification in experimental kidney histopathology,.
DyMorph-B2I: Dynamic and Morphology-Guided Binary-to-Instance Segmentation for Renal Pathology Deep learning–based segmentation and quantification in experimental kidney histopathology,
Reference 10
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-05T18:05:16.713436Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation dd3c6204-ea79-41c4-8dd5-a34413b9cb3f · outbound
This paper cites PySpatial: A High-Speed Whole Slide Image Pathomics Toolkit.
DyMorph-B2I: Dynamic and Morphology-Guided Binary-to-Instance Segmentation for Renal Pathology PySpatial: A High-Speed Whole Slide Image Pathomics Toolkit
Reference 12
- Resolution
- verified exact
- local_arxiv, observed 2026-08-05T18:05:14.870264Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation e5f91deb-ab48-4759-9f74-09c54a5306ab · outbound
This paper cites Spatial pathomics toolkit for quantitative analysis of podocyte nuclei with histology and spatial transcriptomics data in renal pathology,.
DyMorph-B2I: Dynamic and Morphology-Guided Binary-to-Instance Segmentation for Renal Pathology Spatial pathomics toolkit for quantitative analysis of podocyte nuclei with histology and spatial transcriptomics data in renal pathology,
Reference 13
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-05T18:05:16.415149Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation cbe8f659-b828-4978-84c4-f68539c43bdc · outbound
This paper cites Incorporating intratumoral heterogeneity into weakly-supervised deep learning models via variance pooling,.
DyMorph-B2I: Dynamic and Morphology-Guided Binary-to-Instance Segmentation for Renal Pathology Incorporating intratumoral heterogeneity into weakly-supervised deep learning models via variance pooling,
Reference 14
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-05T18:05:16.235607Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 7ad81c83-60b6-4d08-adf5-1e57cc27ab46 · outbound
This paper cites Automatic detection of rare pathologies in fundus photographs using few-shot learning,.
DyMorph-B2I: Dynamic and Morphology-Guided Binary-to-Instance Segmentation for Renal Pathology Automatic detection of rare pathologies in fundus photographs using few-shot learning,
Reference 15
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-05T18:05:16.039097Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 80988e20-d6d0-4301-998e-76a23e322ba7 · outbound
This paper cites Glo-in-one- v2: holistic identification of glomerular cells, tissues, and lesions in human and mouse histopathology,.
DyMorph-B2I: Dynamic and Morphology-Guided Binary-to-Instance Segmentation for Renal Pathology Glo-in-one- v2: holistic identification of glomerular cells, tissues, and lesions in human and mouse histopathology,
Reference 16
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-05T18:05:15.882825Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation cfded7e9-f8d3-471a-b2a4-b083c78e5f43 · outbound
This paper cites A deep learning-based approach for glomeruli instance segmentation from multistained renal biopsy pathologic images,.
DyMorph-B2I: Dynamic and Morphology-Guided Binary-to-Instance Segmentation for Renal Pathology A deep learning-based approach for glomeruli instance segmentation from multistained renal biopsy pathologic images,
Reference 17
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-05T18:05:16.565176Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation b1e02ee9-b077-4d8f-a59c-4d0b0e060060 · outbound
This paper cites An efficient immersion-based watershed transform method and its prototype ar- chitecture,.
DyMorph-B2I: Dynamic and Morphology-Guided Binary-to-Instance Segmentation for Renal Pathology An efficient immersion-based watershed transform method and its prototype ar- chitecture,
Reference 18
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-05T18:05:15.690489Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation a77bef84-9d13-43d5-b777-2c43641b585a · outbound
This paper cites Image analysis using mathematical morphology,.
DyMorph-B2I: Dynamic and Morphology-Guided Binary-to-Instance Segmentation for Renal Pathology Image analysis using mathematical morphology,
Reference 19
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-05T18:05:15.568030Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 776a5332-c61a-410c-ab88-0ced2f012f3b · outbound
This paper cites A fast parallel algorithm for thinning digital patterns,.
DyMorph-B2I: Dynamic and Morphology-Guided Binary-to-Instance Segmentation for Renal Pathology A fast parallel algorithm for thinning digital patterns,
Reference 20
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-05T18:05:15.390416Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 2a2d770b-a647-44b5-983a-3ee64b197485 · outbound
This paper cites Digital pathology evaluation in the multicenter nephrotic syndrome study network (neptune),.
DyMorph-B2I: Dynamic and Morphology-Guided Binary-to-Instance Segmentation for Renal Pathology Digital pathology evaluation in the multicenter nephrotic syndrome study network (neptune),
Reference 21
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-05T18:05:15.201202Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation e68882b2-1976-4e1d-a7e3-576447fe3dfd · outbound
This paper cites Topological structural analysis of digitized binary images by border following,.
DyMorph-B2I: Dynamic and Morphology-Guided Binary-to-Instance Segmentation for Renal Pathology Topological structural analysis of digitized binary images by border following,
Reference 22
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-05T18:05:15.074062Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Pith citing papers
Observation c9382521-831d-447e-8721-8005c63c8b52 · inbound
MORI-Seg: Learning Morphological Geometry for Instance Segmentation without Instance Annotations cites this paper.
MORI-Seg: Learning Morphological Geometry for Instance Segmentation without Instance Annotations DyMorph-B2I: Dynamic and Morphology-Guided Binary-to-Instance Segmentation for Renal Pathology
Reference 35
- Resolution
- verified exact
- arxiv_id, observed 2026-06-29T13:53:28.487419Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.