Paper Citation Record · LEDGER

Object Detection for Medical Image Analysis: Insights from the RT-DETR Model

As of 17 August 2026, this Paper Citation Record lists 10 of 10 outbound references and 0 inbound Pith citation observations for arXiv:2501.16469.

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

pith.paper-citation-record.v1
2501.16469 v1

Coverage vector

measured 10 of 10 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T13:05:46.315926Z

measured 10 of 10 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+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

10 of 10 outbound references displayed

verified exact0
verified fuzzy8
unresolved2
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 82a4e1c0-e4da-48a6-b22a-e8328714ff62 · outbound

This paper cites Automated diabetic retinopathy grading and lesion detection based on the modified R-FCN object-detection algorithm[J].

Object Detection for Medical Image Analysis: Insights from the RT-DETR Model Automated diabetic retinopathy grading and lesion detection based on the modified R-FCN object-detection algorithm[J]

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T13:05:46.485477Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 92c69a31-560a-46f8-a929-227fa3b50be4 · outbound

This paper cites Customised artificial intelligence toolbox for detecting diabetic retinopathy with confocal truecolor fundus images using object detection methods[J].

Object Detection for Medical Image Analysis: Insights from the RT-DETR Model Customised artificial intelligence toolbox for detecting diabetic retinopathy with confocal truecolor fundus images using object detection methods[J]

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T13:05:46.472303Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation a38e5be9-6ea3-46a3-8675-f7e7bfe77b2e · outbound

This paper cites A faster RCNN-based diabetic retinopathy detection method using fused features from retina images[J].

Object Detection for Medical Image Analysis: Insights from the RT-DETR Model A faster RCNN-based diabetic retinopathy detection method using fused features from retina images[J]

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T13:05:46.459072Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation ffb2bedd-3cb9-4cc0-9eb2-3f6a834739b4 · outbound

This paper cites Diabetic retinopathy detection and grading of retinal fundus images using coyote optimization algorithm with deep learning[J].

Object Detection for Medical Image Analysis: Insights from the RT-DETR Model Diabetic retinopathy detection and grading of retinal fundus images using coyote optimization algorithm with deep learning[J]

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T13:05:46.444865Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 8d5022b2-3358-408f-be68-a1812b2b413d · outbound

This paper cites A survey on recent developments in diabetic retinopathy detection through integration of deep learning[J].

Object Detection for Medical Image Analysis: Insights from the RT-DETR Model A survey on recent developments in diabetic retinopathy detection through integration of deep learning[J]

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T13:05:46.428823Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 2a363a11-004f-454c-b890-c4dc1209fc4b · outbound

This paper cites A new method based on deep learning to detect lesions in retinal images using YOLOv5[C]//2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM).

Object Detection for Medical Image Analysis: Insights from the RT-DETR Model A new method based on deep learning to detect lesions in retinal images using YOLOv5[C]//2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T13:05:46.413643Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 5d60bff9-03b3-4a1c-bd03-8b952eb7257b · outbound

This paper cites Diabetic Retinopathy Features Segmentation without Coding Experience with Computer Vision Models YOLOv8 and YOLOv9[J].

Object Detection for Medical Image Analysis: Insights from the RT-DETR Model Diabetic Retinopathy Features Segmentation without Coding Experience with Computer Vision Models YOLOv8 and YOLOv9[J]

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T13:05:46.398754Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation c2314794-503f-4e9c-91ef-a393e2a7a12c · outbound

This paper cites Ssd: Single shot multibox detector[C]//Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11–14, 2016, Proceedings, Part I.

Object Detection for Medical Image Analysis: Insights from the RT-DETR Model Ssd: Single shot multibox detector[C]//Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11–14, 2016, Proceedings, Part I

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T13:05:46.383920Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 744ca79d-9936-4187-93a3-9e5e5ce0bf9e · outbound

This paper cites Deformable DETR: Deformable Transformers for End-to-End Object Detection.

Object Detection for Medical Image Analysis: Insights from the RT-DETR Model Deformable DETR: Deformable Transformers for End-to-End Object Detection

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-10T13:05:46.315926Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bfbf2d3f-26e9-4833-b328-183abe579e73 · outbound

This paper cites an unresolved cited work.

Object Detection for Medical Image Analysis: Insights from the RT-DETR Model Unresolved cited work

Reference 14

Resolution: unresolved; raw_fallback, observed 2026-08-10T13:05:46.367458Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Pith citing papers

No inbound Pith citation observations are available.