Paper Citation Record · LEDGER

A Novel Focal Tversky loss function with improved Attention U-Net for lesion segmentation

As of 15 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:1810.07842.

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

pith.paper-citation-record.v1
1810.07842 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 5 of 5 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-14T12:17:41.144088Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-13T21:43:18.785352Z

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 8952b6f2-f9b8-45b0-bf4e-fa4998b76633 · inbound

More unlabelled data or label more data? A study on semi-supervised laparoscopic image segmentation cites this paper.

More unlabelled data or label more data? A study on semi-supervised laparoscopic image segmentation A Novel Focal Tversky loss function with improved Attention U-Net for lesion segmentation

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-14T12:17:41.144088Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5009e668-6bf9-4de4-aaa9-ee2404faa22b · inbound

TAFM-Net: A Novel Approach to Skin Lesion Segmentation Using Transformer Attention and Focal Modulation cites this paper.

TAFM-Net: A Novel Approach to Skin Lesion Segmentation Using Transformer Attention and Focal Modulation A Novel Focal Tversky loss function with improved Attention U-Net for lesion segmentation

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-12T12:02:57.737885Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ce196006-90de-4717-a05d-e74b0c26839d · 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 A Novel Focal Tversky loss function with improved Attention U-Net for lesion segmentation

Reference 2

Resolution: verified exact; arxiv_id, observed 2026-07-04T23:08:17.537546Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation b068626a-b3e7-4a1c-bde4-703a51af1c89 · inbound

SAGE-GAN: Towards Realistic and Robust Segmentation of Spatially Ordered Nanoparticles via Attention-Guided GANs cites this paper.

SAGE-GAN: Towards Realistic and Robust Segmentation of Spatially Ordered Nanoparticles via Attention-Guided GANs A Novel Focal Tversky loss function with improved Attention U-Net for lesion segmentation

Reference 1

Resolution: verified exact; arxiv_id, observed 2026-07-04T23:08:17.537546Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 5127ee74-acc8-4670-b8e2-3dc3bf0e9b74 · inbound

A 3D SAM-Based Progressive Prompting Framework for Multi-Task Segmentation of Radiotherapy-induced Normal Tissue Injuries in Limited-Data Settings cites this paper.

A 3D SAM-Based Progressive Prompting Framework for Multi-Task Segmentation of Radiotherapy-induced Normal Tissue Injuries in Limited-Data Settings A Novel Focal Tversky loss function with improved Attention U-Net for lesion segmentation

Reference 1

Resolution: metadata mismatch; arxiv_id, observed 2026-07-04T23:08:17.537546Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.