Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-15T15:49:15.757672Z
Paper Citation Record · LEDGER
Johnson-Lindenstrauss Lemma Guided Network for Efficient 3D Medical Segmentation
As of 19 August 2026, this Paper Citation Record lists 24 of 24 outbound references and 0 inbound Pith citation observations for arXiv:2509.22307.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
Reference resolution
24 of 24 outbound references displayed
- verified exact3
- verified fuzzy6
- unresolved13
- parse uncertain0
- malformed identifier2
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation 3ba8e3af-eb5f-4850-90bd-09442579056b · outbound
This paper cites Mamba: Linear-Time Sequence Modeling with Selective State Spaces.
Johnson-Lindenstrauss Lemma Guided Network for Efficient 3D Medical Segmentation Mamba: Linear-Time Sequence Modeling with Selective State Spaces
Reference 5
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T15:49:15.619316Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation b1cb341f-a7b4-484e-9edb-60d51ab010aa · outbound
This paper cites On the Expressive Power of Self-Attention Matrices.
Johnson-Lindenstrauss Lemma Guided Network for Efficient 3D Medical Segmentation On the Expressive Power of Self-Attention Matrices
Reference 7
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T15:49:15.637004Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 31753c6f-3e24-4519-9b06-3b3c7c21705c · outbound
This paper cites Our model improves the Dice by 1.72% compared to the state-of-the-art SuperLightNet.
Johnson-Lindenstrauss Lemma Guided Network for Efficient 3D Medical Segmentation Our model improves the Dice by 1.72% compared to the state-of-the-art SuperLightNet
Reference 9
- Resolution
- malformed identifier
- raw_fallback, observed 2026-08-15T15:49:15.984820Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation a7458eb6-0ca2-44e7-831b-b873a5275506 · outbound
This paper cites KAN: Kolmogorov-Arnold Networks.
Johnson-Lindenstrauss Lemma Guided Network for Efficient 3D Medical Segmentation KAN: Kolmogorov-Arnold Networks
Reference 10
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T15:49:15.661708Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation ff1900d5-76cf-4925-bd67-31ca0523e657 · outbound
This paper cites RWKV: Reinventing RNNs for the Transformer Era.
Johnson-Lindenstrauss Lemma Guided Network for Efficient 3D Medical Segmentation RWKV: Reinventing RNNs for the Transformer Era
Reference 13
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T15:49:15.682835Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation ac9cdfbd-01cc-4ca5-9059-d6834163e1cf · outbound
This paper cites Super-resolution and infection edge detection co-guided learning for covid-19 ct segmentation.
Johnson-Lindenstrauss Lemma Guided Network for Efficient 3D Medical Segmentation Super-resolution and infection edge detection co-guided learning for covid-19 ct segmentation
Reference 14
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T15:49:16.599989Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 9636f1d8-8938-4424-869e-b3457cc2bf33 · outbound
This paper cites Abdelrahman M Shaker, Muhammad Maaz, Hanoona Rasheed, Salman Khan, Ming-Hsuan Yang, and Fahad Shahbaz Khan.
Johnson-Lindenstrauss Lemma Guided Network for Efficient 3D Medical Segmentation Abdelrahman M Shaker, Muhammad Maaz, Hanoona Rasheed, Salman Khan, Ming-Hsuan Yang, and Fahad Shahbaz Khan
Reference 15
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T15:49:15.696124Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation a45413a7-e442-4f4c-9ef0-85c8249f5aae · outbound
This paper cites A large annotated medical image dataset for the development and evaluation of segmentation algorithms.
Johnson-Lindenstrauss Lemma Guided Network for Efficient 3D Medical Segmentation A large annotated medical image dataset for the development and evaluation of segmentation algorithms
Reference 17
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T15:49:15.708651Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 0d7e0b7a-d7f0-4d65-b4b4-9034ae52f329 · outbound
This paper cites Instance Normalization: The Missing Ingredient for Fast Stylization.
Johnson-Lindenstrauss Lemma Guided Network for Efficient 3D Medical Segmentation Instance Normalization: The Missing Ingredient for Fast Stylization
Reference 18
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T15:49:15.715001Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation d423caf4-1352-435e-ada1-70388fbd8359 · outbound
This paper cites Sam-med3d: A vision foundation model for general-purpose segmentation on volumetric medical images.IEEE Transactions on Neural Net- works and Learning Systems, 2025a.
Johnson-Lindenstrauss Lemma Guided Network for Efficient 3D Medical Segmentation Sam-med3d: A vision foundation model for general-purpose segmentation on volumetric medical images.IEEE Transactions on Neural Net- works and Learning Systems, 2025a
Reference 19
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T15:49:16.580180Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation dfe3559d-d394-465e-a79a-6777bd18576c · outbound
This paper cites U-RWKV: Lightweight medical image segmentation with direction-adaptive RWKV.
Johnson-Lindenstrauss Lemma Guided Network for Efficient 3D Medical Segmentation U-RWKV: Lightweight medical image segmentation with direction-adaptive RWKV
Reference 20
- Resolution
- verified exact
- local_arxiv, observed 2026-08-15T15:49:16.109901Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation e99ef6a9-6dd0-4c58-a15a-ba4ad8d2292c · outbound
This paper cites README.md.
Johnson-Lindenstrauss Lemma Guided Network for Efficient 3D Medical Segmentation README.md
Reference 21
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T15:49:16.561379Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 6672a929-8b38-4b31-becd-a3bceffc659e · outbound
This paper cites Notably,r,B k 1 , andS k 1 are closely related to the computational cost, and the specific settings for different datasets are given in Appendix D.3.
Johnson-Lindenstrauss Lemma Guided Network for Efficient 3D Medical Segmentation Notably,r,B k 1 , andS k 1 are closely related to the computational cost, and the specific settings for different datasets are given in Appendix D.3
Reference 22
- Resolution
- malformed identifier
- raw_fallback, observed 2026-08-15T15:49:16.544319Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation dcff5851-0dcd-45a2-8cad-480dc9273a78 · outbound
This paper cites MP.”: Million Parameters; “GF.
Johnson-Lindenstrauss Lemma Guided Network for Efficient 3D Medical Segmentation MP.”: Million Parameters; “GF
Reference 23
- Resolution
- verified exact
- raw_fallback, observed 2026-08-15T15:49:16.076045Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation c7dc704f-3355-4d76-b2f8-02c6c6b68dea · outbound
This paper cites Does dinov3 set a new medical vision standard?arXiv preprint arXiv:2509.06467,.
Johnson-Lindenstrauss Lemma Guided Network for Efficient 3D Medical Segmentation Does dinov3 set a new medical vision standard?arXiv preprint arXiv:2509.06467,
Reference 1984
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T15:49:15.647895Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation b8ab91e1-b4c7-470e-a1db-4354a7099b1b · outbound
This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.
Johnson-Lindenstrauss Lemma Guided Network for Efficient 3D Medical Segmentation An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
Reference 2016
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T15:49:15.603889Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 285eb53f-7f10-4c59-8424-5000e6a33ac4 · outbound
This paper cites 3d u-net: learning dense volumetric segmentation from sparse annotation.
Johnson-Lindenstrauss Lemma Guided Network for Efficient 3D Medical Segmentation 3d u-net: learning dense volumetric segmentation from sparse annotation
Reference 2017
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T15:49:16.674138Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation c19661b1-0d21-4d8a-bf0e-93d43e0e44a7 · outbound
This paper cites The multimodal brain tumor image segmentation benchmark (brats).IEEE transactions on medical imaging, 34(10):1993–2024,.
Johnson-Lindenstrauss Lemma Guided Network for Efficient 3D Medical Segmentation The multimodal brain tumor image segmentation benchmark (brats).IEEE transactions on medical imaging, 34(10):1993–2024,
Reference 2019
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T15:49:16.619997Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 4a3a5774-84ef-4739-abc9-90a060f4f46c · outbound
This paper cites Towards Lightweight Hyperspectral Image Super-Resolution with Depthwise Separable Dilated Convolutional Network.
Johnson-Lindenstrauss Lemma Guided Network for Efficient 3D Medical Segmentation Towards Lightweight Hyperspectral Image Super-Resolution with Depthwise Separable Dilated Convolutional Network
Reference 2020
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T15:49:15.676096Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 494a2824-9dac-4688-b686-96cc8a7a658d · outbound
This paper cites Super convergence cosine annealing with warm-up learning rate.
Johnson-Lindenstrauss Lemma Guided Network for Efficient 3D Medical Segmentation Super convergence cosine annealing with warm-up learning rate
Reference 2021
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T15:49:16.646641Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 435a1719-6a35-4605-82f4-18648a2e6a79 · outbound
This paper cites The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation and Radiogenomic Classification.
Johnson-Lindenstrauss Lemma Guided Network for Efficient 3D Medical Segmentation The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation and Radiogenomic Classification
Reference 2022
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T15:49:15.584152Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 24be8909-68de-41a5-a206-caf735d9a0ff · outbound
This paper cites Pruning Filters for Efficient ConvNets.
Johnson-Lindenstrauss Lemma Guided Network for Efficient 3D Medical Segmentation Pruning Filters for Efficient ConvNets
Reference 2023
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T15:49:15.611642Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 0cefe14e-23ee-46e7-b7d6-8cd6ba2bd128 · outbound
This paper cites DINOv3.
Johnson-Lindenstrauss Lemma Guided Network for Efficient 3D Medical Segmentation DINOv3
Reference 2024
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T15:49:15.701536Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 6ad22a35-c684-4c94-812e-1ab3bbe88cbc · outbound
This paper cites HCMA-UNet: A Hybrid CNN-Mamba UNet with Axial Self-Attention for Efficient Breast Cancer Segmentation.
Johnson-Lindenstrauss Lemma Guided Network for Efficient 3D Medical Segmentation HCMA-UNet: A Hybrid CNN-Mamba UNet with Axial Self-Attention for Efficient Breast Cancer Segmentation
Reference 2025
- Resolution
- verified exact
- local_arxiv, observed 2026-08-15T15:49:16.454924Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Pith citing papers
No inbound Pith citation observations are available.