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Paper Citation Record · LEDGER

KM-UNet KAN Mamba UNet for medical image segmentation

As of 14 August 2026, this Paper Citation Record lists 35 of 35 outbound references and 0 inbound Pith citation observations for arXiv:2501.02559.

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

pith.paper-citation-record.v1
2501.02559 v1

Coverage vector

measured 35 of 35 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T22:12:57.152313Z

measured 35 of 35 standing notices

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Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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Reference resolution

35 of 35 outbound references displayed

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  • verified fuzzy18
  • unresolved13
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External citation measurements

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Outbound references

Observation b6a1c901-da8c-4dfc-8584-1411c82a7cc0 · outbound

This paper cites Deep Learning in Medical Image Analysis.

KM-UNet KAN Mamba UNet for medical image segmentation Deep Learning in Medical Image Analysis

Reference 1

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Observation d7414311-c0f4-478c-810e-1fa5a37a6cd3 · outbound

This paper cites Few -Shot Medical Image Segmentation Using a Global Correlation Network with Discriminative Embedding.

KM-UNet KAN Mamba UNet for medical image segmentation Few -Shot Medical Image Segmentation Using a Global Correlation Network with Discriminative Embedding

Reference 2

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Observation 84150a56-b7bc-477a-bd97-1aaa7e56ebd6 · outbound

This paper cites Hierarchical Deep Network with Uncertainty-Aware Semi-Supervised Learning for Vessel Segmentation.

KM-UNet KAN Mamba UNet for medical image segmentation Hierarchical Deep Network with Uncertainty-Aware Semi-Supervised Learning for Vessel Segmentation

Reference 3

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Observation dc658e2a-71e1-4b7f-a9ae-77a23c90707e · outbound

This paper cites Medical Image Segmentation Using Deep Learning: A Survey.

KM-UNet KAN Mamba UNet for medical image segmentation Medical Image Segmentation Using Deep Learning: A Survey

Reference 5

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Observation 9b20f2ae-e265-4eec-8a77-5f0def3fdf42 · outbound

This paper cites Unsupervised Anomaly Segmentation Using Image-Semantic Cycle Translation.

KM-UNet KAN Mamba UNet for medical image segmentation Unsupervised Anomaly Segmentation Using Image-Semantic Cycle Translation

Reference 6

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Observation eaadf8f2-95c7-413f-9fa2-178b20f4aec2 · outbound

This paper cites UNet++: A Nested U-Net Architecture for Medical Image Segmentation.

KM-UNet KAN Mamba UNet for medical image segmentation UNet++: A Nested U-Net Architecture for Medical Image Segmentation

Reference 7

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Observation 067bc1ef-d388-4f94-a1fe-c4ef110837d6 · outbound

This paper cites V -Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation.

KM-UNet KAN Mamba UNet for medical image segmentation V -Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation

Reference 8

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Observation a80f83c5-c681-4d56-a5bc-cdb1bf81283f · outbound

This paper cites Do Vision Transformers See Like Convolutional Neural Networks.

KM-UNet KAN Mamba UNet for medical image segmentation Do Vision Transformers See Like Convolutional Neural Networks

Reference 9

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Observation f72afea1-1c67-4f1b-a88c-7a558de58e5d · outbound

This paper cites Global Context Vision Transformers.

KM-UNet KAN Mamba UNet for medical image segmentation Global Context Vision Transformers

Reference 10

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Observation d28d2863-f7b0-4840-99f7-40ed6731e664 · outbound

This paper cites TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation.

KM-UNet KAN Mamba UNet for medical image segmentation TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation

Reference 11

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Observation b94c641e-767f-4c98-add8-99ab31de1d19 · outbound

This paper cites Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images.

KM-UNet KAN Mamba UNet for medical image segmentation Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images

Reference 12

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Observation 8222134a-f43f-4d34-b0da-08081ee1e847 · outbound

This paper cites An Image Is Worth 16x16 Words: Transformers for Image Recognition at Scale.

KM-UNet KAN Mamba UNet for medical image segmentation An Image Is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 13

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Observation 1e5f7ad7-8693-4140-85b3-6c44b5b57918 · outbound

This paper cites an unresolved cited work.

KM-UNet KAN Mamba UNet for medical image segmentation Unresolved cited work

Reference 14

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Observation e1d4647d-5ca4-40b0-ae6d-a5a7a6c655e7 · outbound

This paper cites Training Data -Efficient Image Transformers & Distillation through Attention.

KM-UNet KAN Mamba UNet for medical image segmentation Training Data -Efficient Image Transformers & Distillation through Attention

Reference 15

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Observation 8f2510b7-4635-4913-901a-a7be7a974f04 · outbound

This paper cites Hungry Hungry Hippos: Towards Language Modeling with State Space Models.

KM-UNet KAN Mamba UNet for medical image segmentation Hungry Hungry Hippos: Towards Language Modeling with State Space Models

Reference 16

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Observation c80e016d-5112-4cfc-90c3-ac27f1adf41a · outbound

This paper cites U-Mamba: Enhancing Long-Range Dependency for Biomedical Image Segmentation.

KM-UNet KAN Mamba UNet for medical image segmentation U-Mamba: Enhancing Long-Range Dependency for Biomedical Image Segmentation

Reference 17

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Observation 33254bdf-d6f8-4b72-8e40-0d54a09ae87c · outbound

This paper cites RWKV: Reinventing RNNs for the Transformer Era.

KM-UNet KAN Mamba UNet for medical image segmentation RWKV: Reinventing RNNs for the Transformer Era

Reference 18

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Observation 7914205c-b609-4bf3-98ad-7679d7d240c6 · outbound

This paper cites SegMamba: Long-range Sequential Modeling Mamba For 3D Medical Image Segmentation.

KM-UNet KAN Mamba UNet for medical image segmentation SegMamba: Long-range Sequential Modeling Mamba For 3D Medical Image Segmentation

Reference 19

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Observation 2937a5e8-700a-4985-9d27-d49569b5b2fb · outbound

This paper cites White-Box Transformers via Sparse Rate Reduction.

KM-UNet KAN Mamba UNet for medical image segmentation White-Box Transformers via Sparse Rate Reduction

Reference 20

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Observation e86755a6-2fd4-423b-ac27-488f1b2466d1 · outbound

This paper cites U -Net: Convolutional Networks for Biomedical Image Segmentation.

KM-UNet KAN Mamba UNet for medical image segmentation U -Net: Convolutional Networks for Biomedical Image Segmentation

Reference 21

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Observation e4c13d82-4d79-472a-8bf5-9c1612f71713 · outbound

This paper cites 3D MRI Brain Tumor Segmentation Using Autoencoder Regularization.

KM-UNet KAN Mamba UNet for medical image segmentation 3D MRI Brain Tumor Segmentation Using Autoencoder Regularization

Reference 22

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Observation 7be8506b-bd75-40c4-bc37-8ad122277fa7 · outbound

This paper cites Mamba: Linear-Time Sequence Modeling with Selective State Spaces.

KM-UNet KAN Mamba UNet for medical image segmentation Mamba: Linear-Time Sequence Modeling with Selective State Spaces

Reference 23

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Observation ea1a483e-a6da-4407-ab57-231a33bf15b2 · outbound

This paper cites ECA-Net: EfficientChannel Attention for Deep Convolutional Neural Networks.In CVPR, 2020.

KM-UNet KAN Mamba UNet for medical image segmentation ECA-Net: EfficientChannel Attention for Deep Convolutional Neural Networks.In CVPR, 2020

Reference 24

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Observation a4ea113d-a417-4669-992c-dad4bf2d7a51 · outbound

This paper cites Dataset of Breast Ultrasound Images.

KM-UNet KAN Mamba UNet for medical image segmentation Dataset of Breast Ultrasound Images

Reference 25

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Observation 00069bd7-a761-47d1-8402-666e7a390465 · outbound

This paper cites Medical Transformer: Gated Axial-Attention for Medical Image Segmentation.

KM-UNet KAN Mamba UNet for medical image segmentation Medical Transformer: Gated Axial-Attention for Medical Image Segmentation

Reference 26

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Observation 8a11d052-1c72-4508-be53-9041fc08bbf1 · outbound

This paper cites WM -DOVA Maps for Accurate Polyp Highlighting in Colonoscopy: Validation vs. Saliency Maps from Physicians.

KM-UNet KAN Mamba UNet for medical image segmentation WM -DOVA Maps for Accurate Polyp Highlighting in Colonoscopy: Validation vs. Saliency Maps from Physicians

Reference 27

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Observation 59dbce26-b393-4691-b4d0-af05599eedb6 · outbound

This paper cites ISIC 2017 - Skin Lesion Analysis Towards Melanoma Detection.

KM-UNet KAN Mamba UNet for medical image segmentation ISIC 2017 - Skin Lesion Analysis Towards Melanoma Detection

Reference 28

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Observation 7836d593-e11b-488e-b3c8-4120763886af · outbound

This paper cites Skin Lesion Analysis Toward Melanoma Detection 2018: A Challenge Hosted by the International Skin Imaging Collaboration (ISIC).

KM-UNet KAN Mamba UNet for medical image segmentation Skin Lesion Analysis Toward Melanoma Detection 2018: A Challenge Hosted by the International Skin Imaging Collaboration (ISIC)

Reference 29

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Observation 4fd492a7-991e-423d-a7d3-029f37fcc7f8 · outbound

This paper cites In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM).

KM-UNet KAN Mamba UNet for medical image segmentation In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

Reference 30

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Observation c3b6c8d4-28e8-40de-a299-c0b199026332 · outbound

This paper cites Journal of Medical Imaging 6(1), 014006– 014006 (2019).

KM-UNet KAN Mamba UNet for medical image segmentation Journal of Medical Imaging 6(1), 014006– 014006 (2019)

Reference 31

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Observation f27110f9-4136-46b3-9656-f6090660679a · outbound

This paper cites In: International Workshop on PRedictive Intelligence In MEdicine.

KM-UNet KAN Mamba UNet for medical image segmentation In: International Workshop on PRedictive Intelligence In MEdicine

Reference 32

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

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Observation eb5a7bce-8f39-437e-8d62-6e2fc92596d3 · outbound

This paper cites In: Proc.

KM-UNet KAN Mamba UNet for medical image segmentation In: Proc

Reference 33

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Source-reported events for the cited work

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Observation 6b585174-0757-4a0f-ae87-31ac34f13732 · outbound

This paper cites an unresolved cited work.

KM-UNet KAN Mamba UNet for medical image segmentation Unresolved cited work

Reference 34

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Observation ceaa010d-2503-4443-95b1-51efdc32c1d7 · outbound

This paper cites U-KAN Makes Strong Backbone for Medical Image Segmentation and Generation.

KM-UNet KAN Mamba UNet for medical image segmentation U-KAN Makes Strong Backbone for Medical Image Segmentation and Generation

Reference 35

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Observation 9258b66e-4b80-43db-a395-63948265d5f9 · outbound

This paper cites VM-UNet: Vision Mamba UNet for Medical Image Segmentation.

KM-UNet KAN Mamba UNet for medical image segmentation VM-UNet: Vision Mamba UNet for Medical Image Segmentation

Reference 36

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T22:12:57.152313Z digest=sha256:5923cfe6f5d03cc33d48546b855d76b0a4df429b127e682344896ada0ec27713

Pith citing papers

No inbound Pith citation observations are available.