Paper Citation Record · LEDGER

Dante: An Open Source Model Pre-Training and Fine-Tuning Tool for the Dafne Federated Framework for Medical Image Segmentation

As of 5 August 2026, this Paper Citation Record lists 22 of 22 outbound references and 0 inbound Pith citation observations for arXiv:2605.03602.

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

pith.paper-citation-record.v1
2605.03602 v1

Coverage vector

measured 22 of 22 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-07T13:02:41.464847Z

measured 22 of 22 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-05T06:32:48.257954+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

22 of 22 outbound references displayed

verified exact18
verified fuzzy1
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch3

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation c4ad6055-8d96-487f-bcaa-e1cf18d1545f · outbound

This paper cites Generalist models in medical image segmentation: A survey and performance comparison with task-specific approaches.

Dante: An Open Source Model Pre-Training and Fine-Tuning Tool for the Dafne Federated Framework for Medical Image Segmentation Generalist models in medical image segmentation: A survey and performance comparison with task-specific approaches

Reference 1

Resolution: verified exact; arxiv_id, observed 2026-05-09T05:35:24.299915Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 8a2ff75c-6b47-41ad-86ce-7ef7f934d725 · outbound

This paper cites A narrative review of foundation models for medical image segmentation: zero-shot performance evaluation on diverse modalities.

Dante: An Open Source Model Pre-Training and Fine-Tuning Tool for the Dafne Federated Framework for Medical Image Segmentation A narrative review of foundation models for medical image segmentation: zero-shot performance evaluation on diverse modalities

Reference 2

Resolution: verified exact; doi, observed 2026-05-09T05:35:24.405674Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 80a0b62b-f01c-4ad8-a437-5d241086f14a · outbound

This paper cites Deep Learning-Based 3D and 2D Approaches for Skeletal Muscle Segmentation on Low-Dose CT Images.

Dante: An Open Source Model Pre-Training and Fine-Tuning Tool for the Dafne Federated Framework for Medical Image Segmentation Deep Learning-Based 3D and 2D Approaches for Skeletal Muscle Segmentation on Low-Dose CT Images

Reference 3

Resolution: verified exact; doi, observed 2026-05-09T05:35:24.394338Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 3ce54055-edbc-4034-a162-885a9b47989a · outbound

This paper cites Nature Communications15(1), 654 (1 2024).

Dante: An Open Source Model Pre-Training and Fine-Tuning Tool for the Dafne Federated Framework for Medical Image Segmentation Nature Communications15(1), 654 (1 2024)

Reference 4

Resolution: metadata mismatch; doi, observed 2026-05-09T05:35:24.319376Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation af10ec2f-470e-4a42-9a5e-30dae8b88f16 · outbound

This paper cites TotalSegmentator: Robust Segmentation of 104 Anatomic Structures in CT Images.

Dante: An Open Source Model Pre-Training and Fine-Tuning Tool for the Dafne Federated Framework for Medical Image Segmentation TotalSegmentator: Robust Segmentation of 104 Anatomic Structures in CT Images

Reference 5

Resolution: verified exact; doi, observed 2026-05-09T05:35:24.296253Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 1ca36328-edb6-43d7-a24e-2678877362b0 · outbound

This paper cites Domain Adaptation for Medical Image Analysis: A Survey.

Dante: An Open Source Model Pre-Training and Fine-Tuning Tool for the Dafne Federated Framework for Medical Image Segmentation Domain Adaptation for Medical Image Analysis: A Survey

Reference 6

Resolution: verified exact; arxiv_id, observed 2026-05-09T05:35:24.338700Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation c745ea9a-5d8c-4b84-aa98-248c4d2097b7 · outbound

This paper cites Transfer learning in medical image segmentation: New insights from analysis of the dynamics of model parameters and learned representations.

Dante: An Open Source Model Pre-Training and Fine-Tuning Tool for the Dafne Federated Framework for Medical Image Segmentation Transfer learning in medical image segmentation: New insights from analysis of the dynamics of model parameters and learned representations

Reference 7

Resolution: verified exact; arxiv_id, observed 2026-05-09T05:35:24.449338Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation c587c26b-9116-41af-814f-7d8463e505d3 · outbound

This paper cites Deep Anatomical Federated Network (Dafne): An Open Client-Server Framework for Continuous, Collaborative Improvement of Deep Learning–based Medical Image Segmentation.

Dante: An Open Source Model Pre-Training and Fine-Tuning Tool for the Dafne Federated Framework for Medical Image Segmentation Deep Anatomical Federated Network (Dafne): An Open Client-Server Framework for Continuous, Collaborative Improvement of Deep Learning–based Medical Image Segmentation

Reference 8

Resolution: verified exact; doi, observed 2026-05-09T05:35:24.305620Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation eab42d00-532b-4762-85c8-05968032238c · outbound

This paper cites LoRA: Low-Rank Adaptation of Large Language Models.

Dante: An Open Source Model Pre-Training and Fine-Tuning Tool for the Dafne Federated Framework for Medical Image Segmentation LoRA: Low-Rank Adaptation of Large Language Models

Reference 9

Resolution: metadata mismatch; local_arxiv, observed 2026-05-09T05:35:24.332877Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation e69f83ce-b80b-411e-8a40-146fd5ac00eb · outbound

This paper cites Customized Segment Anything Model for Medical Image Segmentation.

Dante: An Open Source Model Pre-Training and Fine-Tuning Tool for the Dafne Federated Framework for Medical Image Segmentation Customized Segment Anything Model for Medical Image Segmentation

Reference 10

Resolution: metadata mismatch; arxiv_id, observed 2026-05-09T05:35:24.308419Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation ccc915c8-cd69-426d-9175-e88c2c771754 · outbound

This paper cites Convolution Meets LoRA: Parameter Efficient Finetuning for Segment Anything Model.

Dante: An Open Source Model Pre-Training and Fine-Tuning Tool for the Dafne Federated Framework for Medical Image Segmentation Convolution Meets LoRA: Parameter Efficient Finetuning for Segment Anything Model

Reference 11

Resolution: verified exact; arxiv_id, observed 2026-05-09T05:35:24.326616Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 96d38d26-45ee-4472-91a3-1ae2460c8f6e · outbound

This paper cites Parameter-Efficient Fine-Tuning for Medical Image Analysis: The Missed Opportunity.

Dante: An Open Source Model Pre-Training and Fine-Tuning Tool for the Dafne Federated Framework for Medical Image Segmentation Parameter-Efficient Fine-Tuning for Medical Image Analysis: The Missed Opportunity

Reference 12

Resolution: verified exact; arxiv_id, observed 2026-05-09T05:35:24.322482Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 307865cf-af88-4855-996a-ff8891a8024f · outbound

This paper cites Towards Foundation Models and Few-Shot Parameter-Efficient Fine-Tuning for Volumetric Organ Segmentation.

Dante: An Open Source Model Pre-Training and Fine-Tuning Tool for the Dafne Federated Framework for Medical Image Segmentation Towards Foundation Models and Few-Shot Parameter-Efficient Fine-Tuning for Volumetric Organ Segmentation

Reference 13

Resolution: verified exact; arxiv_id, observed 2026-05-09T05:35:24.368835Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation bb6e2fd5-9935-4b87-85d3-d01279339fa4 · outbound

This paper cites MRISegmenter: A Fully Accurate and Robust Automated Multiorgan and Structure Segmentation Tool for T1-weighted Abdominal MRI.

Dante: An Open Source Model Pre-Training and Fine-Tuning Tool for the Dafne Federated Framework for Medical Image Segmentation MRISegmenter: A Fully Accurate and Robust Automated Multiorgan and Structure Segmentation Tool for T1-weighted Abdominal MRI

Reference 14

Resolution: verified exact; doi, observed 2026-05-09T05:35:24.329275Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 48831a24-16f3-454d-81be-a0eecc8808f7 · outbound

This paper cites How transferable are features in deep neural networks?.

Dante: An Open Source Model Pre-Training and Fine-Tuning Tool for the Dafne Federated Framework for Medical Image Segmentation How transferable are features in deep neural networks?

Reference 15

Resolution: verified exact; arxiv_id, observed 2026-05-09T05:35:24.314097Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation abf058d0-f313-4f8a-b594-1689e2a45835 · outbound

This paper cites nnU-Net: Self-adapting Framework for U-Net-Based Medical Image Segmentation.

Dante: An Open Source Model Pre-Training and Fine-Tuning Tool for the Dafne Federated Framework for Medical Image Segmentation nnU-Net: Self-adapting Framework for U-Net-Based Medical Image Segmentation

Reference 16

Resolution: verified exact; arxiv_id, observed 2026-05-09T05:35:24.302985Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 03056362-0f6d-4a44-b12f-7dfd305647c3 · outbound

This paper cites MONAI: An open-source framework for deep learning in healthcare.

Dante: An Open Source Model Pre-Training and Fine-Tuning Tool for the Dafne Federated Framework for Medical Image Segmentation MONAI: An open-source framework for deep learning in healthcare

Reference 17

Resolution: verified exact; arxiv_id, observed 2026-05-13T22:54:28.721435Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 913eef51-4bf5-434d-9a9b-ac233eb72747 · outbound

This paper cites Three Mechanisms of Weight Decay Regularization.

Dante: An Open Source Model Pre-Training and Fine-Tuning Tool for the Dafne Federated Framework for Medical Image Segmentation Three Mechanisms of Weight Decay Regularization

Reference 18

Resolution: verified exact; arxiv_id, observed 2026-05-09T05:35:24.421547Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation ab78ecef-1c44-4641-8442-87fcd9239e54 · outbound

This paper cites AMOS: A Large-Scale Abdominal Multi- Organ Benchmark for Versatile Medical Image Segmentation.

Dante: An Open Source Model Pre-Training and Fine-Tuning Tool for the Dafne Federated Framework for Medical Image Segmentation AMOS: A Large-Scale Abdominal Multi- Organ Benchmark for Versatile Medical Image Segmentation

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-05-27T11:59:05.660514Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 6abbbad4-efc6-4a96-a69f-02d5ee8ea073 · outbound

This paper cites CHAOS - Combined (CT-MR) Healthy Abdominal Organ Segmentation Challenge Data [Internet].

Dante: An Open Source Model Pre-Training and Fine-Tuning Tool for the Dafne Federated Framework for Medical Image Segmentation CHAOS - Combined (CT-MR) Healthy Abdominal Organ Segmentation Challenge Data [Internet]

Reference 20

Resolution: verified exact; doi, observed 2026-05-09T05:35:24.480279Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 8b9a3e83-dc2e-4ab4-bdec-f62a64247edd · outbound

This paper cites MSLesSeg: baseline and benchmarking of a new Multiple Sclerosis Lesion Segmentation dataset.Scientific Data, 12(1):920, May 2025.

Dante: An Open Source Model Pre-Training and Fine-Tuning Tool for the Dafne Federated Framework for Medical Image Segmentation MSLesSeg: baseline and benchmarking of a new Multiple Sclerosis Lesion Segmentation dataset.Scientific Data, 12(1):920, May 2025

Reference 21

Resolution: verified exact; doi, observed 2026-05-09T05:35:24.316567Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 1e4142bd-1ad4-4840-9d01-c160f130e4a6 · outbound

This paper cites Longitudinal multiple sclerosis lesion segmentation data resource.

Dante: An Open Source Model Pre-Training and Fine-Tuning Tool for the Dafne Federated Framework for Medical Image Segmentation Longitudinal multiple sclerosis lesion segmentation data resource

Reference 22

Resolution: verified exact; doi, observed 2026-05-09T05:35:24.473640Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Pith citing papers

No inbound Pith citation observations are available.