Paper Citation Record · LEDGER

Tversky loss function for image segmentation using 3D fully convolutional deep networks

As of 11 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 9 inbound Pith citation observations for arXiv:1706.05721.

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

pith.paper-citation-record.v1
1706.05721 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 9 of 9 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 9 of 9 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T22:32:36.922685Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-06-30T12:54:40.284106Z

Reference resolution

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External citation measurements

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Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

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

Multimodal HIE Lesion Segmentation in Neonates: A Comparative Study of Loss Functions cites this paper.

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 0f02472f-3ca2-4a7d-b23b-daa4e6fbf6b6 · inbound

Surg-SegFormer: A Dual Transformer-Based Model for Holistic Surgical Scene Segmentation cites this paper.

Surg-SegFormer: A Dual Transformer-Based Model for Holistic Surgical Scene Segmentation Tversky loss function for image segmentation using 3D fully convolutional deep networks

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-06T19:53:27.316391Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cfedc502-10ae-430c-aee3-3f6cd8009df1 · inbound

Resource-Efficient Glioma Segmentation on Sub-Saharan MRI cites this paper.

Resource-Efficient Glioma Segmentation on Sub-Saharan MRI Tversky loss function for image segmentation using 3D fully convolutional deep networks

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-04T19:06:48.680956Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d7da281c-a1d7-43fb-a304-6992d337bd4a · inbound

Adversarial Attacks on Downstream Weather Forecasting Models: Application to Tropical Cyclone Trajectory Prediction cites this paper.

Adversarial Attacks on Downstream Weather Forecasting Models: Application to Tropical Cyclone Trajectory Prediction Tversky loss function for image segmentation using 3D fully convolutional deep networks

Reference 9

Resolution: verified exact; local_arxiv, observed 2026-05-21T20:10:34.428693Z

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 7a6f07a6-bd98-4d07-ba17-35926f2ce254 · inbound

An Explainable Vision-Language Model Framework with Adaptive PID-Tversky Loss for Lumbar Spinal Stenosis Diagnosis cites this paper.

An Explainable Vision-Language Model Framework with Adaptive PID-Tversky Loss for Lumbar Spinal Stenosis Diagnosis Tversky loss function for image segmentation using 3D fully convolutional deep networks

Reference 35

Resolution: verified exact; arxiv_id, observed 2026-05-13T21:43:18.807235Z

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 92e7ae65-9e5a-4a0d-a59f-5c3e450359c1 · inbound

Component-Adaptive and Lesion-Level Supervision for Improved Small Structure Segmentation in Brain MRI cites this paper.

Component-Adaptive and Lesion-Level Supervision for Improved Small Structure Segmentation in Brain MRI Tversky loss function for image segmentation using 3D fully convolutional deep networks

Reference 6

Resolution: verified exact; arxiv_id, observed 2026-05-11T00:20:55.192046Z

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 7c8901f1-8e79-434b-a64e-daf536ef2467 · inbound

Low-Data Supervised Adaptation Outperforms Prompting for Cloud Segmentation Under Domain Shift cites this paper.

Low-Data Supervised Adaptation Outperforms Prompting for Cloud Segmentation Under Domain Shift Tversky loss function for image segmentation using 3D fully convolutional deep networks

Reference 26

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T07:41:01.040232Z

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 b604743f-93f7-4498-8607-14101a14c2ac · inbound

SAMamba3D: adapting Segment Anything for generalizable 3D segmentation of multiphase pore-scale images cites this paper.

SAMamba3D: adapting Segment Anything for generalizable 3D segmentation of multiphase pore-scale images Tversky loss function for image segmentation using 3D fully convolutional deep networks

Reference 38

Resolution: verified exact; arxiv_id, observed 2026-05-11T15:21:10.355204Z

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 59d7836e-bbc5-414c-bfef-b55e33a82990 · inbound

RADIANT-PET: Reasoning-Augmented PET/CT Lesion Segmentation with Large Language Models and Reinforcement Learning cites this paper.

RADIANT-PET: Reasoning-Augmented PET/CT Lesion Segmentation with Large Language Models and Reinforcement Learning Tversky loss function for image segmentation using 3D fully convolutional deep networks

Reference 10

Resolution: verified exact; local_arxiv, observed 2026-06-30T12:54:40.285431Z

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.