Paper Citation Record · LEDGER

Multimodal HIE Lesion Segmentation in Neonates: A Comparative Study of Loss Functions

As of 8 August 2026, this Paper Citation Record lists 11 of 11 outbound references and 0 inbound Pith citation observations for arXiv:2502.09148.

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

pith.paper-citation-record.v1
2502.09148 v1

Coverage vector

measured 11 of 11 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T22:32:36.945058Z

measured 11 of 11 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+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

11 of 11 outbound references displayed

verified exact1
verified fuzzy3
unresolved5
parse uncertain0
malformed identifier1
metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 45b1a6ed-31d9-44a8-b823-a6bc981f6cf6 · outbound

This paper cites Rethinking dice loss for medical image segmentation.

Multimodal HIE Lesion Segmentation in Neonates: A Comparative Study of Loss Functions Rethinking dice loss for medical image segmentation

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T22:32:37.206890Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 176b28e9-801c-40a1-b0d2-d77f770e47ba · outbound

This paper cites Deep learning to achieve clinically applicable segmentation of head and neck anatomy for radiotherapy.

Multimodal HIE Lesion Segmentation in Neonates: A Comparative Study of Loss Functions Deep learning to achieve clinically applicable segmentation of head and neck anatomy for radiotherapy

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-07T22:32:36.938274Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2b666277-2870-4314-b284-bc78d1bcebd2 · outbound

This paper cites Intrapartum-related neonatal encephalopathy incidence and impairment at regional and global levels for 2010 with trends from.

Multimodal HIE Lesion Segmentation in Neonates: A Comparative Study of Loss Functions Intrapartum-related neonatal encephalopathy incidence and impairment at regional and global levels for 2010 with trends from

Reference 2008

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T22:32:37.219568Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 91c374a1-30d5-41e3-9bd6-274d5a981a19 · outbound

This paper cites an unresolved cited work.

Multimodal HIE Lesion Segmentation in Neonates: A Comparative Study of Loss Functions Unresolved cited work

Reference 2011

Resolution: unresolved; no resolver link, observed 2026-08-07T22:32:36.909746Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 12b0896b-e7ef-409e-9432-ffc33aac9fea · outbound

This paper cites U-Net: Convolutional Networks for Biomedical Image Segmentation.

Multimodal HIE Lesion Segmentation in Neonates: A Comparative Study of Loss Functions U-Net: Convolutional Networks for Biomedical Image Segmentation

Reference 2015

Resolution: unresolved; no resolver link, observed 2026-08-07T22:32:36.931041Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9ad1c71d-0c3f-45e5-ae31-cef21802cb58 · outbound

This paper cites Tversky loss function for image segmentation using 3D fully convolutional deep networks.

Multimodal HIE Lesion Segmentation in Neonates: A Comparative Study of Loss Functions Tversky loss function for image segmentation using 3D fully convolutional deep networks

Reference 2017

Resolution: unresolved; no resolver link, observed 2026-08-07T22:32:36.922685Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2abdfefc-fb66-45a2-9300-009e5b6e9d3f · outbound

This paper cites an unresolved cited work.

Multimodal HIE Lesion Segmentation in Neonates: A Comparative Study of Loss Functions Unresolved cited work

Reference 2018

Resolution: verified exact; doi, observed 2026-08-07T22:32:36.996400Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation a01adb20-d69c-4534-9aee-c25875d07a34 · outbound

This paper cites URL https://doi.

Multimodal HIE Lesion Segmentation in Neonates: A Comparative Study of Loss Functions URL https://doi

Reference 2020

Resolution: metadata mismatch; raw_fallback, observed 2026-08-07T22:32:37.153495Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 8b44d32e-858d-49f1-8f63-b217a3207a3f · outbound

This paper cites A survey of loss functions for semantic segmentation.

Multimodal HIE Lesion Segmentation in Neonates: A Comparative Study of Loss Functions A survey of loss functions for semantic segmentation

Reference 2021

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T22:32:37.193815Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 7ddde314-0acd-4939-b39e-ed7b16a42657 · outbound

This paper cites Ernest M Graham, Kristin A Ruis, Adam L Hartman, Frances J Northington, and Harold E Fox.

Multimodal HIE Lesion Segmentation in Neonates: A Comparative Study of Loss Functions Ernest M Graham, Kristin A Ruis, Adam L Hartman, Frances J Northington, and Harold E Fox

Reference 2023

Resolution: malformed identifier; raw_fallback, observed 2026-08-07T22:32:37.232078Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 151a27e7-94a2-42b7-ac96-90453ebec9ff · outbound

This paper cites Medical SAM 2: Segment medical images as video via Segment Anything Model 2.

Multimodal HIE Lesion Segmentation in Neonates: A Comparative Study of Loss Functions Medical SAM 2: Segment medical images as video via Segment Anything Model 2

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-07T22:32:36.945058Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.