Paper Citation Record · LEDGER

Automated Design of Deep Learning Methods for Biomedical Image Segmentation

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 8 inbound Pith citation observations for arXiv:1904.08128.

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

pith.paper-citation-record.v1
1904.08128 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 8 of 8 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 8 of 8 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T18:20:58.639113Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-24T01:43:42.906108Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 02eed068-adce-47a4-9711-15ca7cf27ef1 · inbound

Implantable Adaptive Cells: A Novel Enhancement for Pre-Trained U-Nets in Medical Image Segmentation cites this paper.

Implantable Adaptive Cells: A Novel Enhancement for Pre-Trained U-Nets in Medical Image Segmentation Automated Design of Deep Learning Methods for Biomedical Image Segmentation

Reference 26

Resolution: verified exact; arxiv_id, observed 2026-05-24T01:43:42.910839Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 5e9b7374-fcb3-4719-9469-ec89fb8cc20a · inbound

Self-Prompt SAM: Medical Image Segmentation via Automatic Prompt SAM Adaptation cites this paper.

Self-Prompt SAM: Medical Image Segmentation via Automatic Prompt SAM Adaptation Automated Design of Deep Learning Methods for Biomedical Image Segmentation

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-09T18:20:58.639113Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8498b51a-aad2-4115-8144-f67503f85e09 · inbound

RFMedSAM 2: Automatic Prompt Refinement for Enhanced Volumetric Medical Image Segmentation with SAM 2 cites this paper.

RFMedSAM 2: Automatic Prompt Refinement for Enhanced Volumetric Medical Image Segmentation with SAM 2 Automated Design of Deep Learning Methods for Biomedical Image Segmentation

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-09T11:21:45.766112Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c211362a-c130-4ac0-973d-8799cb8ab124 · inbound

Good Enough? An Investigation on the Impact of Label Quality in Large-Scale Medical Datasets cites this paper.

Good Enough? An Investigation on the Impact of Label Quality in Large-Scale Medical Datasets Automated Design of Deep Learning Methods for Biomedical Image Segmentation

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-07T13:47:30.538833Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 074ad526-81e2-4be5-864b-354308319d0e · inbound

A Comprehensive Study on Medical Image Segmentation using Deep Neural Networks cites this paper.

A Comprehensive Study on Medical Image Segmentation using Deep Neural Networks Automated Design of Deep Learning Methods for Biomedical Image Segmentation

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-07T10:50:51.169378Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 53491502-9c68-4747-a7f2-853fcfb63e38 · inbound

Predicting Brain Tumor Response to Therapy using a Hybrid Deep Learning and Radiomics Approach cites this paper.

Predicting Brain Tumor Response to Therapy using a Hybrid Deep Learning and Radiomics Approach Automated Design of Deep Learning Methods for Biomedical Image Segmentation

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-04T23:33:09.222215Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d032612b-65dd-4ccb-8fa4-1c66c928e4ac · inbound

PUUMA (Placental patch and whole-Uterus dual-branch U-Mamba-based Architecture): Functional MRI Prediction of Gestational Age at Birth and Preterm Risk cites this paper.

PUUMA (Placental patch and whole-Uterus dual-branch U-Mamba-based Architecture): Functional MRI Prediction of Gestational Age at Birth and Preterm Risk Automated Design of Deep Learning Methods for Biomedical Image Segmentation

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-04T23:33:25.542614Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 72b9426b-8271-4404-be8b-44ea28db4670 · inbound

ZScribbleSeg: A comprehensive segmentation framework with modeling of efficient annotation and maximization of scribble supervision cites this paper.

ZScribbleSeg: A comprehensive segmentation framework with modeling of efficient annotation and maximization of scribble supervision Automated Design of Deep Learning Methods for Biomedical Image Segmentation

Reference 15

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T18:51:08.548096Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.