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Paper Citation Record · LEDGER

Mamba-Sea: A Mamba-based Framework with Global-to-Local Sequence Augmentation for Generalizable Medical Image Segmentation

As of 17 August 2026, this Paper Citation Record lists 79 of 79 outbound references and 0 inbound Pith citation observations for arXiv:2504.17515.

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

pith.paper-citation-record.v1
2504.17515 v1

Coverage vector

measured 79 of 79 reference resolution

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measured 79 of 79 standing notices

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Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

79 of 79 outbound references displayed

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External citation measurements

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Outbound references

Observation edd8e33e-21ed-4d78-b515-d2f7535bae18 · outbound

This paper cites U-net: Convolutional networks for biomedical image segmentation,.

Mamba-Sea: A Mamba-based Framework with Global-to-Local Sequence Augmentation for Generalizable Medical Image Segmentation U-net: Convolutional networks for biomedical image segmentation,

Reference 1

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Observation a6ce0535-ccef-4382-bebc-cf5377aaba0d · outbound

This paper cites H-denseunet: hybrid densely connected unet for liver and tumor segmentation from ct volumes,.

Mamba-Sea: A Mamba-based Framework with Global-to-Local Sequence Augmentation for Generalizable Medical Image Segmentation H-denseunet: hybrid densely connected unet for liver and tumor segmentation from ct volumes,

Reference 2

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Observation e3c9ce8f-235e-4898-aa12-55e42bff9135 · outbound

This paper cites Segmentation of arm ultrasound images in breast cancer- related lymphedema: A database and deep learning algorithm,.

Mamba-Sea: A Mamba-based Framework with Global-to-Local Sequence Augmentation for Generalizable Medical Image Segmentation Segmentation of arm ultrasound images in breast cancer- related lymphedema: A database and deep learning algorithm,

Reference 3

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Observation 04474b9b-59c1-404b-a3ea-7afd9b777399 · outbound

This paper cites Shape-aware meta-learning for generalizing prostate mri segmentation to unseen domains,.

Mamba-Sea: A Mamba-based Framework with Global-to-Local Sequence Augmentation for Generalizable Medical Image Segmentation Shape-aware meta-learning for generalizing prostate mri segmentation to unseen domains,

Reference 4

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Observation aef5c9b7-1560-42a8-a02d-863e51773af8 · outbound

This paper cites Cddsa: Contrastive domain disentanglement and style augmentation for generalizable medical image segmentation,.

Mamba-Sea: A Mamba-based Framework with Global-to-Local Sequence Augmentation for Generalizable Medical Image Segmentation Cddsa: Contrastive domain disentanglement and style augmentation for generalizable medical image segmentation,

Reference 5

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Observation 7c1ec331-ea56-4275-99b3-c5b90bf9ca1d · outbound

This paper cites Swin-unet: Unet-like pure transformer for medical image segmenta- tion,.

Mamba-Sea: A Mamba-based Framework with Global-to-Local Sequence Augmentation for Generalizable Medical Image Segmentation Swin-unet: Unet-like pure transformer for medical image segmenta- tion,

Reference 6

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Observation 0cbfbe19-6e94-4f0d-aa7a-e1f8a22e8118 · outbound

This paper cites Medical sam adapter: Adapting segment anything model for medical image segmentation,.

Mamba-Sea: A Mamba-based Framework with Global-to-Local Sequence Augmentation for Generalizable Medical Image Segmentation Medical sam adapter: Adapting segment anything model for medical image segmentation,

Reference 7

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Observation 3f59f1d9-ade5-4732-9d9d-8f5a597f0af0 · outbound

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

Mamba-Sea: A Mamba-based Framework with Global-to-Local Sequence Augmentation for Generalizable Medical Image Segmentation VM-UNet: Vision Mamba UNet for Medical Image Segmentation

Reference 8

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Observation d1da633d-23ca-4705-8e50-f6d943d086a2 · outbound

This paper cites Semi-supervised meta-learning with disentanglement for domain-generalised medical image segmentation,.

Mamba-Sea: A Mamba-based Framework with Global-to-Local Sequence Augmentation for Generalizable Medical Image Segmentation Semi-supervised meta-learning with disentanglement for domain-generalised medical image segmentation,

Reference 9

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Observation 545937fd-ff92-425a-aca7-5fd652be944e · outbound

This paper cites Structure- preserving color normalization and sparse stain separation for histolog- ical images,.

Mamba-Sea: A Mamba-based Framework with Global-to-Local Sequence Augmentation for Generalizable Medical Image Segmentation Structure- preserving color normalization and sparse stain separation for histolog- ical images,

Reference 10

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Observation 1c5d279d-8a83-45a7-9985-b4a1ab2a0373 · outbound

This paper cites Domain gen- eralization in restoration of cataract fundus images via high-frequency components,.

Mamba-Sea: A Mamba-based Framework with Global-to-Local Sequence Augmentation for Generalizable Medical Image Segmentation Domain gen- eralization in restoration of cataract fundus images via high-frequency components,

Reference 11

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Observation df93b262-bbc7-4404-ae2f-04c0144ae741 · outbound

This paper cites Generalizable cross- modality medical image segmentation via style augmentation and dual normalization,.

Mamba-Sea: A Mamba-based Framework with Global-to-Local Sequence Augmentation for Generalizable Medical Image Segmentation Generalizable cross- modality medical image segmentation via style augmentation and dual normalization,

Reference 12

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Observation 4c81d5e4-7414-49ca-9efb-fd46ed6e59fb · outbound

This paper cites Domain generalization for mammography detection via multi-style and multi-view contrastive learning,.

Mamba-Sea: A Mamba-based Framework with Global-to-Local Sequence Augmentation for Generalizable Medical Image Segmentation Domain generalization for mammography detection via multi-style and multi-view contrastive learning,

Reference 13

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Observation 11b157ec-190c-4827-be0f-37a9ff870e71 · outbound

This paper cites Aadg: Automatic augmentation for domain generalization on retinal image segmentation,.

Mamba-Sea: A Mamba-based Framework with Global-to-Local Sequence Augmentation for Generalizable Medical Image Segmentation Aadg: Automatic augmentation for domain generalization on retinal image segmentation,

Reference 14

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Observation 00447a6a-d9ed-4783-a587-18bd588b5574 · outbound

This paper cites Improving vision transformers by revisiting high-frequency components,.

Mamba-Sea: A Mamba-based Framework with Global-to-Local Sequence Augmentation for Generalizable Medical Image Segmentation Improving vision transformers by revisiting high-frequency components,

Reference 15

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Observation 02f598fb-fe2b-4442-b05a-c408e328f73c · outbound

This paper cites Dimix: Disentangle-and-mix based do- main generalizable medical image segmentation,.

Mamba-Sea: A Mamba-based Framework with Global-to-Local Sequence Augmentation for Generalizable Medical Image Segmentation Dimix: Disentangle-and-mix based do- main generalizable medical image segmentation,

Reference 16

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Observation acb31aad-9e3c-4447-a7e9-8d302c286480 · outbound

This paper cites Transformers in medical imaging: A survey,.

Mamba-Sea: A Mamba-based Framework with Global-to-Local Sequence Augmentation for Generalizable Medical Image Segmentation Transformers in medical imaging: A survey,

Reference 17

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Observation 47f14524-a1f7-4fbc-932c-2b7acd37572d · outbound

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

Mamba-Sea: A Mamba-based Framework with Global-to-Local Sequence Augmentation for Generalizable Medical Image Segmentation Mamba: Linear-Time Sequence Modeling with Selective State Spaces

Reference 18

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Observation c56c02f2-e717-4a5e-98d7-b96fc102b37d · outbound

This paper cites Log-vmamba: Local- global vision mamba for medical image segmentation,.

Mamba-Sea: A Mamba-based Framework with Global-to-Local Sequence Augmentation for Generalizable Medical Image Segmentation Log-vmamba: Local- global vision mamba for medical image segmentation,

Reference 19

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Observation fd5aa107-d7aa-407b-bc76-532c63d436f7 · outbound

This paper cites Mambavision: A hybrid mamba- transformer vision backbone,.

Mamba-Sea: A Mamba-based Framework with Global-to-Local Sequence Augmentation for Generalizable Medical Image Segmentation Mambavision: A hybrid mamba- transformer vision backbone,

Reference 20

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Observation 5a04f8d0-f4ff-4e43-9d66-089748cbaa11 · outbound

This paper cites VSSD: Vision Mamba with Non-Causal State Space Duality.

Mamba-Sea: A Mamba-based Framework with Global-to-Local Sequence Augmentation for Generalizable Medical Image Segmentation VSSD: Vision Mamba with Non-Causal State Space Duality

Reference 21

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Observation fa95669e-ffb3-4c15-9d95-a69fbea9c6ea · outbound

This paper cites MedMamba: Vision Mamba for Medical Image Classification.

Mamba-Sea: A Mamba-based Framework with Global-to-Local Sequence Augmentation for Generalizable Medical Image Segmentation MedMamba: Vision Mamba for Medical Image Classification

Reference 22

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Observation d49c02cd-7e24-4b4e-a262-d8c26d5a4019 · outbound

This paper cites Vision mamba for classification of breast ultrasound images,.

Mamba-Sea: A Mamba-based Framework with Global-to-Local Sequence Augmentation for Generalizable Medical Image Segmentation Vision mamba for classification of breast ultrasound images,

Reference 23

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Observation cfbd343e-1ae5-4d7a-add7-1bbb89f9f534 · outbound

This paper cites Mamba-UNet: UNet-Like Pure Visual Mamba for Medical Image Segmentation.

Mamba-Sea: A Mamba-based Framework with Global-to-Local Sequence Augmentation for Generalizable Medical Image Segmentation Mamba-UNet: UNet-Like Pure Visual Mamba for Medical Image Segmentation

Reference 24

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Observation 407ed772-ffcd-41fe-836f-b01459064cbd · outbound

This paper cites Polyp-mamba: Polyp segmentation with visual mamba,.

Mamba-Sea: A Mamba-based Framework with Global-to-Local Sequence Augmentation for Generalizable Medical Image Segmentation Polyp-mamba: Polyp segmentation with visual mamba,

Reference 25

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Observation 07ee793c-11a4-4f39-8501-3e9095aeb3bf · outbound

This paper cites Segmamba: Long-range sequential modeling mamba for 3d medical image segmentation,.

Mamba-Sea: A Mamba-based Framework with Global-to-Local Sequence Augmentation for Generalizable Medical Image Segmentation Segmamba: Long-range sequential modeling mamba for 3d medical image segmentation,

Reference 26

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Observation 6d5f4d3d-a854-487b-82fb-85ebec8d5fe0 · outbound

This paper cites HC-Mamba: Vision MAMBA with Hybrid Convolutional Techniques for Medical Image Segmentation.

Mamba-Sea: A Mamba-based Framework with Global-to-Local Sequence Augmentation for Generalizable Medical Image Segmentation HC-Mamba: Vision MAMBA with Hybrid Convolutional Techniques for Medical Image Segmentation

Reference 27

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Observation 203e2639-17d2-4be6-9dd1-06cb4ca5f260 · outbound

This paper cites UltraLight VM-UNet: Parallel Vision Mamba Significantly Reduces Parameters for Skin Lesion Segmentation.

Mamba-Sea: A Mamba-based Framework with Global-to-Local Sequence Augmentation for Generalizable Medical Image Segmentation UltraLight VM-UNet: Parallel Vision Mamba Significantly Reduces Parameters for Skin Lesion Segmentation

Reference 28

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Observation fd7fa598-522b-408a-8269-3ffebd568ded · outbound

This paper cites Segment anything,.

Mamba-Sea: A Mamba-based Framework with Global-to-Local Sequence Augmentation for Generalizable Medical Image Segmentation Segment anything,

Reference 29

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Observation 9ac9b206-7d9f-4850-9070-069b8267ceda · outbound

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

Mamba-Sea: A Mamba-based Framework with Global-to-Local Sequence Augmentation for Generalizable Medical Image Segmentation LoRA: Low-Rank Adaptation of Large Language Models

Reference 30

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Observation 3c82feed-0281-45ad-9235-3f8c99cf658d · outbound

This paper cites Multi- centre, multi-vendor and multi-disease cardiac segmentation: the m&ms challenge,.

Mamba-Sea: A Mamba-based Framework with Global-to-Local Sequence Augmentation for Generalizable Medical Image Segmentation Multi- centre, multi-vendor and multi-disease cardiac segmentation: the m&ms challenge,

Reference 31

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Observation c7115b0e-6070-4c15-908f-c12af868a975 · outbound

This paper cites Dofe: Domain-oriented feature embedding for generalizable fundus image segmentation on unseen datasets,.

Mamba-Sea: A Mamba-based Framework with Global-to-Local Sequence Augmentation for Generalizable Medical Image Segmentation Dofe: Domain-oriented feature embedding for generalizable fundus image segmentation on unseen datasets,

Reference 32

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Source-reported events for the cited work

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Observation 9f2343ce-698b-45d2-8eaa-c92b044e8e5d · outbound

This paper cites Minimax estimation of maximum mean discrepancy with radial kernels,.

Mamba-Sea: A Mamba-based Framework with Global-to-Local Sequence Augmentation for Generalizable Medical Image Segmentation Minimax estimation of maximum mean discrepancy with radial kernels,

Reference 33

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raw_fallback, observed 2026-08-16T10:42:23.858889Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-16T10:42:22.954242Z digest=sha256:15f4f617dc374c2204f1572a4a151acc5a201a7c50ffd3a211e1663d8c08f9aa

Observation 17fedb07-3665-473f-bc42-3ca83e26f5fb · outbound

This paper cites VMamba: Visual state space model,.

Mamba-Sea: A Mamba-based Framework with Global-to-Local Sequence Augmentation for Generalizable Medical Image Segmentation VMamba: Visual state space model,

Reference 34

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-16T10:42:22.959085Z digest=sha256:a1e4b3ea9a4ee5cfd65af05e82aae2647fd2b71f109813fc4b4253b34dc73569

Observation 5c403a92-fc68-4d35-956a-6e21ea366418 · outbound

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

Mamba-Sea: A Mamba-based Framework with Global-to-Local Sequence Augmentation for Generalizable Medical Image Segmentation U-Mamba: Enhancing Long-range Dependency for Biomedical Image Segmentation

Reference 35

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T10:42:22.963627Z digest=sha256:c476101594a39ef0c2e31686abf7015e251dc38a3e8b64d4721e8d0061780cfa

Observation e338669c-2704-4395-90a6-12939999d2dc · outbound

This paper cites Start: A generalized state space model with saliency-driven token-aware transformation,.

Mamba-Sea: A Mamba-based Framework with Global-to-Local Sequence Augmentation for Generalizable Medical Image Segmentation Start: A generalized state space model with saliency-driven token-aware transformation,

Reference 36

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-16T10:42:22.968253Z digest=sha256:16c7bd3206b003990fbc0491a34098bce3d0963da8248a1dc6395864bdec36f8

Observation 3aed54db-94a3-4de7-b118-1d915b00897d · outbound

This paper cites Domain and content adaptive convolution based multi-source domain generalization for medical image segmentation,.

Mamba-Sea: A Mamba-based Framework with Global-to-Local Sequence Augmentation for Generalizable Medical Image Segmentation Domain and content adaptive convolution based multi-source domain generalization for medical image segmentation,

Reference 37

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raw_fallback, observed 2026-08-16T10:42:23.817848Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-16T10:42:22.972418Z digest=sha256:008117d014383a43bca2c89506a464bb0abe4db3aaec9266fd7e4dce3f4590ae

Observation 74c1fd08-627d-48c6-b1fe-aa48bc69d140 · outbound

This paper cites Feddg: Federated domain generalization on medical image segmentation via episodic learning in continuous frequency space,.

Mamba-Sea: A Mamba-based Framework with Global-to-Local Sequence Augmentation for Generalizable Medical Image Segmentation Feddg: Federated domain generalization on medical image segmentation via episodic learning in continuous frequency space,

Reference 38

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verified fuzzy
raw_fallback, observed 2026-08-16T10:42:23.803110Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-16T10:42:22.976665Z digest=sha256:c29adaf62932b6721cbcb1e19d8269f098235f12006bc32f2d7ecd3a4fa7e252

Observation 7b1d0b9d-6c65-4379-b1ca-22bdaec1e136 · outbound

This paper cites Do- main generalization via model-agnostic learning of semantic features,.

Mamba-Sea: A Mamba-based Framework with Global-to-Local Sequence Augmentation for Generalizable Medical Image Segmentation Do- main generalization via model-agnostic learning of semantic features,

Reference 39

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T10:42:22.981092Z digest=sha256:41c9ca33e3a8d6b401dd2d97a3338a504b02f851c28c4ef6e31bfd0b90044bf1

Observation 3a835301-08e5-4570-9333-cb0b61ed831b · outbound

This paper cites Random style transfer based domain generalization net- works integrating shape and spatial information,.

Mamba-Sea: A Mamba-based Framework with Global-to-Local Sequence Augmentation for Generalizable Medical Image Segmentation Random style transfer based domain generalization net- works integrating shape and spatial information,

Reference 40

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raw_fallback, observed 2026-08-16T10:42:23.779501Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-16T10:42:22.985167Z digest=sha256:642e70cced691212b03b573309cc65e09667f44e3d7a0ecd071241c5b4cb2da7

Observation e15e5bff-ac89-4343-ac15-233e03426484 · outbound

This paper cites Tesla: Test-time self-learning with automatic adversarial augmentation,.

Mamba-Sea: A Mamba-based Framework with Global-to-Local Sequence Augmentation for Generalizable Medical Image Segmentation Tesla: Test-time self-learning with automatic adversarial augmentation,

Reference 41

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raw_fallback, observed 2026-08-16T10:42:23.765408Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-16T10:42:22.989811Z digest=sha256:420a634666e017ab47d6063cb48ae9ec91be09189bc49a21c0f2907efe39afab

Observation d9c585d6-6d39-4de7-950b-7d2b96777a5a · outbound

This paper cites Vm-unet-v2: Rethinking vision mamba unet for medical image segmentation,.

Mamba-Sea: A Mamba-based Framework with Global-to-Local Sequence Augmentation for Generalizable Medical Image Segmentation Vm-unet-v2: Rethinking vision mamba unet for medical image segmentation,

Reference 42

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raw_fallback, observed 2026-08-16T10:42:23.752243Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-16T10:42:22.994238Z digest=sha256:be742d12f84baaf0324a611a2274f28119eb2609b249b07035770e1328f3974f

Observation 0ee04a4a-f259-4627-a53b-8549cc62ed02 · outbound

This paper cites LKM-UNet: Large Kernel Vision Mamba UNet for Medical Image Segmentation.

Mamba-Sea: A Mamba-based Framework with Global-to-Local Sequence Augmentation for Generalizable Medical Image Segmentation LKM-UNet: Large Kernel Vision Mamba UNet for Medical Image Segmentation

Reference 43

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T10:42:22.998873Z digest=sha256:3088536b84f0d3b3eaf08d833d823272e34ae3a8ab32c64ede1e5a90b21a9af0

Observation e50d0c4a-36c5-46c4-96ea-078c94eaa180 · outbound

This paper cites ViM-UNet: Vision Mamba for Biomedical Segmentation.

Mamba-Sea: A Mamba-based Framework with Global-to-Local Sequence Augmentation for Generalizable Medical Image Segmentation ViM-UNet: Vision Mamba for Biomedical Segmentation

Reference 44

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T10:42:23.003578Z digest=sha256:54d2cdbb24a25f47c2328bd9711af2612256591c2e5bbde112b7c5b9b7c5277f

Observation 17fed982-3fa1-4fd2-97b1-6b5c1ba19840 · outbound

This paper cites CAMS: Convolution and Attention-Free Mamba-based Cardiac Image Segmentation.

Mamba-Sea: A Mamba-based Framework with Global-to-Local Sequence Augmentation for Generalizable Medical Image Segmentation CAMS: Convolution and Attention-Free Mamba-based Cardiac Image Segmentation

Reference 45

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verified exact
local_arxiv, observed 2026-08-16T10:42:23.241770Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-16T10:42:23.007878Z digest=sha256:5c2e5f9b7703e2a09af088e33055aa7007a26b2114da3642e9fccadbbca4c8ea

Observation 7a23dff3-df50-4bbb-9723-c934d165c817 · outbound

This paper cites Efficiently Modeling Long Sequences with Structured State Spaces.

Mamba-Sea: A Mamba-based Framework with Global-to-Local Sequence Augmentation for Generalizable Medical Image Segmentation Efficiently Modeling Long Sequences with Structured State Spaces

Reference 46

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T10:42:23.012298Z digest=sha256:44fdf13429503229f761b8313282e336bac0d9923cb4ac9846dd4b825dd0bf6e

Observation 73a0f753-b974-4706-ae29-6a4eafd4f330 · outbound

This paper cites nnu-net: a self-configuring method for deep learning-based biomedical image segmentation,.

Mamba-Sea: A Mamba-based Framework with Global-to-Local Sequence Augmentation for Generalizable Medical Image Segmentation nnu-net: a self-configuring method for deep learning-based biomedical image segmentation,

Reference 47

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T10:42:23.016831Z digest=sha256:de5378aa843e8c5ebb91cd6390a7747537b81a3591781aa996d0cef48cf02787

Observation 1d637a5c-9629-4c84-a7fd-b7d49f6de232 · outbound

This paper cites Medsrgan: medical images super-resolution using generative adversarial networks,.

Mamba-Sea: A Mamba-based Framework with Global-to-Local Sequence Augmentation for Generalizable Medical Image Segmentation Medsrgan: medical images super-resolution using generative adversarial networks,

Reference 48

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raw_fallback, observed 2026-08-16T10:42:23.730150Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-16T10:42:23.021070Z digest=sha256:3a8d6e9bcb3c6e0e67f9b83e630d7c862389957609dddb28c4884cf30966e676

Observation a51882fc-89c5-47c3-810d-8c165da4322d · outbound

This paper cites On the proper use of structural similarity for the robust evaluation of medical image synthesis models,.

Mamba-Sea: A Mamba-based Framework with Global-to-Local Sequence Augmentation for Generalizable Medical Image Segmentation On the proper use of structural similarity for the robust evaluation of medical image synthesis models,

Reference 49

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raw_fallback, observed 2026-08-16T10:42:23.715546Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-16T10:42:23.025067Z digest=sha256:710df9e4f67969cb4af40e04a926fbb516494afe8068a65bbc7160137d0f7a82

Observation a8ae90bb-67be-4e2b-9684-327fa38f8319 · outbound

This paper cites Toward fast, flexible, and robust low-light image enhancement,.

Mamba-Sea: A Mamba-based Framework with Global-to-Local Sequence Augmentation for Generalizable Medical Image Segmentation Toward fast, flexible, and robust low-light image enhancement,

Reference 50

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raw_fallback, observed 2026-08-16T10:42:23.700080Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-16T10:42:23.029199Z digest=sha256:eaf92e2dc1d5b83de2a62f2d3ad6a92afb81de827d8196eace0c6da80f1d3927

Observation bf9fd96a-298a-426c-a5c8-590871edb5a2 · outbound

This paper cites Vision mamba: Efficient visual representation learning with bidirectional state space model,.

Mamba-Sea: A Mamba-based Framework with Global-to-Local Sequence Augmentation for Generalizable Medical Image Segmentation Vision mamba: Efficient visual representation learning with bidirectional state space model,

Reference 51

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T10:42:23.033561Z digest=sha256:206a085caf7fefeeb74fe36f0e1230bb6a97061ecc3e70d6dc4824dee82da53e

Observation 6df4a982-062d-4963-b7f0-57b7fe54404d · outbound

This paper cites Domain generalization with mixstyle,.

Mamba-Sea: A Mamba-based Framework with Global-to-Local Sequence Augmentation for Generalizable Medical Image Segmentation Domain generalization with mixstyle,

Reference 52

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T10:42:23.037698Z digest=sha256:372b7563ace98f591699897017eb34960d53ea12cf819c7dd0015b7cc68004cd

Observation 229e7356-a3b2-4040-a5c5-3a68f806e15e · outbound

This paper cites Aloft: A lightweight mlp- like architecture with dynamic low-frequency transform for domain generalization,.

Mamba-Sea: A Mamba-based Framework with Global-to-Local Sequence Augmentation for Generalizable Medical Image Segmentation Aloft: A lightweight mlp- like architecture with dynamic low-frequency transform for domain generalization,

Reference 53

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raw_fallback, observed 2026-08-16T10:42:23.668949Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-16T10:42:23.041694Z digest=sha256:f0643e5d902362f1cc2b28343e6048da72c370207cf73c4b3df1c2088fcb9044

Observation f3405239-024e-4990-8995-e7214ba70d5d · outbound

This paper cites A comprehensive retinal image dataset for the assessment of glaucoma from the optic nerve head analysis,.

Mamba-Sea: A Mamba-based Framework with Global-to-Local Sequence Augmentation for Generalizable Medical Image Segmentation A comprehensive retinal image dataset for the assessment of glaucoma from the optic nerve head analysis,

Reference 54

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T10:42:23.045548Z digest=sha256:0fa78f595ccc5527decfffc4374a8fd9272976e3e2f76f0e91758b3f99542d35

Observation 7cd10198-4e26-48da-9362-5c64b500ded9 · outbound

This paper cites Rim-one: An open retinal image database for optic nerve evaluation,.

Mamba-Sea: A Mamba-based Framework with Global-to-Local Sequence Augmentation for Generalizable Medical Image Segmentation Rim-one: An open retinal image database for optic nerve evaluation,

Reference 55

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raw_fallback, observed 2026-08-16T10:42:23.645712Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-16T10:42:23.049929Z digest=sha256:38c5ebbc8f1534a6f6ade588232af0a77b14485abbd5b38eb48186a4bf76b91b

Observation 71baa9e0-09b8-4659-9799-a66da7b7e080 · outbound

This paper cites Refuge challenge: A unified framework for evaluating automated methods for glaucoma assessment from fundus photographs,.

Mamba-Sea: A Mamba-based Framework with Global-to-Local Sequence Augmentation for Generalizable Medical Image Segmentation Refuge challenge: A unified framework for evaluating automated methods for glaucoma assessment from fundus photographs,

Reference 56

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raw_fallback, observed 2026-08-16T10:42:23.632549Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-16T10:42:23.053920Z digest=sha256:222fc39ef81ca6cb0699a73c4efa173fe8142f746db28e169337698f04dcd22f

Observation 8038ef6e-ef43-4c21-abf7-35bf2f332dfe · outbound

This paper cites Nci- isbi 2013 challenge: automated segmentation of prostate structures,.

Mamba-Sea: A Mamba-based Framework with Global-to-Local Sequence Augmentation for Generalizable Medical Image Segmentation Nci- isbi 2013 challenge: automated segmentation of prostate structures,

Reference 57

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raw_fallback, observed 2026-08-16T10:42:23.619316Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-16T10:42:23.057879Z digest=sha256:60fe226bd07f6d809262af47fe0c3a8e92540210e17c362ed3747d0f5a4ee2b1

Observation f4e84ff1-f6bf-40a4-be18-809467c42140 · outbound

This paper cites Computer-aided detection and diagnosis for prostate cancer based on mono and multi-parametric mri: a review,.

Mamba-Sea: A Mamba-based Framework with Global-to-Local Sequence Augmentation for Generalizable Medical Image Segmentation Computer-aided detection and diagnosis for prostate cancer based on mono and multi-parametric mri: a review,

Reference 58

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raw_fallback, observed 2026-08-16T10:42:23.605731Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-16T10:42:23.061857Z digest=sha256:51bc128e073bfca57a99d12872a5cad13daa79b6cea3f4b59043f4f338964aa8

Observation bbeeb306-a1dd-473c-8fa4-f5ec45bbee00 · outbound

This paper cites Evaluation of prostate segmentation algorithms for mri: the promise12 challenge,.

Mamba-Sea: A Mamba-based Framework with Global-to-Local Sequence Augmentation for Generalizable Medical Image Segmentation Evaluation of prostate segmentation algorithms for mri: the promise12 challenge,

Reference 59

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raw_fallback, observed 2026-08-16T10:42:23.592588Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-16T10:42:23.066004Z digest=sha256:ef1ad2aeb93c4c81a6f5d201ad302db2dc0823ae4cde9bd80818122b215ccda1

Observation 47271869-0871-4f61-b5ba-6d25c73032c1 · outbound

This paper cites Domain generalization by solving jigsaw puzzles,.

Mamba-Sea: A Mamba-based Framework with Global-to-Local Sequence Augmentation for Generalizable Medical Image Segmentation Domain generalization by solving jigsaw puzzles,

Reference 60

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T10:42:23.069998Z digest=sha256:571d3ed6ffbbe87e85736e5fdf4d9031fc64bb0fb5af74a02d085d108ac4821d

Observation 05635d4b-3718-40fd-8b87-de2ed893afe8 · outbound

This paper cites Generalizing deep learning for medical image segmentation to unseen domains via deep stacked transformation,.

Mamba-Sea: A Mamba-based Framework with Global-to-Local Sequence Augmentation for Generalizable Medical Image Segmentation Generalizing deep learning for medical image segmentation to unseen domains via deep stacked transformation,

Reference 61

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raw_fallback, observed 2026-08-16T10:42:23.570501Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-16T10:42:23.073842Z digest=sha256:9b4a29e9f7de0598a21264dd8db6862cc19b0adf77947d6daed3e8c1e5f73c12

Observation 1075a122-e3d4-4c19-91a7-52a6673e17db · outbound

This paper cites Learning robust shape regularization for generalizable medical image segmentation,.

Mamba-Sea: A Mamba-based Framework with Global-to-Local Sequence Augmentation for Generalizable Medical Image Segmentation Learning robust shape regularization for generalizable medical image segmentation,

Reference 62

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raw_fallback, observed 2026-08-16T10:42:23.556965Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-16T10:42:23.077868Z digest=sha256:5cfc62c1223d657911029812420d82ff9d06e3257b53b33f829b64b3cb82d94f

Observation 008857fa-dad0-4fac-b977-b401ed55837d · outbound

This paper cites SAM-Med2D.

Mamba-Sea: A Mamba-based Framework with Global-to-Local Sequence Augmentation for Generalizable Medical Image Segmentation SAM-Med2D

Reference 63

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no resolver link, observed 2026-08-16T10:42:23.081725Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T10:42:23.081725Z digest=sha256:03a028bf920db500e3c7da23a0ea3773b641d09c50c8e7ce705fbf403f022222

Observation 63c24344-cbda-4844-a7dd-2da08386ee4f · outbound

This paper cites Imagenet: A large-scale hierarchical image database,.

Mamba-Sea: A Mamba-based Framework with Global-to-Local Sequence Augmentation for Generalizable Medical Image Segmentation Imagenet: A large-scale hierarchical image database,

Reference 64

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T10:42:23.085967Z digest=sha256:ae8b5f39f2f8495f94b3368948815b72656c68341b9ff2bf0e7fb54a638183f9

Observation dbd71ab7-00ec-4646-9fea-d130b35fe9cd · outbound

This paper cites Cutmix: Reg- ularization strategy to train strong classifiers with localizable features,.

Mamba-Sea: A Mamba-based Framework with Global-to-Local Sequence Augmentation for Generalizable Medical Image Segmentation Cutmix: Reg- ularization strategy to train strong classifiers with localizable features,

Reference 65

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T10:42:23.089896Z digest=sha256:e52c1c5768cd40a6b850160f5f210b01020f06de0f1dc4a4a61d543c286e98b9

Observation c3962fbb-4400-4a57-afe6-d8aea6fa23d1 · outbound

This paper cites Visualizing data using t-sne.

Mamba-Sea: A Mamba-based Framework with Global-to-Local Sequence Augmentation for Generalizable Medical Image Segmentation Visualizing data using t-sne

Reference 66

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T10:42:23.093770Z digest=sha256:b1168766a67e521df51becb049b57909ef81bc2fec9fc4659a40803f7692c5a5

Observation 2f28e9ba-7a3d-4a41-8002-9c8e050266c3 · outbound

This paper cites De-LightSAM: Modality-Decoupled Lightweight SAM for Generalizable Medical Segmentation.

Mamba-Sea: A Mamba-based Framework with Global-to-Local Sequence Augmentation for Generalizable Medical Image Segmentation De-LightSAM: Modality-Decoupled Lightweight SAM for Generalizable Medical Segmentation

Reference 67

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T10:42:23.098262Z digest=sha256:9d48a81ff0ebed427eacf107ad6babddaed31494b3be328410c2535629bb2c65

Observation cecc9b06-ebff-4c29-8c08-9e0fa29d25de · outbound

This paper cites The ham10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions,.

Mamba-Sea: A Mamba-based Framework with Global-to-Local Sequence Augmentation for Generalizable Medical Image Segmentation The ham10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions,

Reference 68

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T10:42:23.102502Z digest=sha256:acd1efe17c29c4c702a17a69b817cbbc60ffb4be435ddc02d7d0be207952088b

Observation 5d4aaee0-30d5-4907-b5d5-cc844fff85bf · outbound

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

Mamba-Sea: A Mamba-based Framework with Global-to-Local Sequence Augmentation for Generalizable Medical Image Segmentation Skin Lesion Analysis Toward Melanoma Detection 2018: A Challenge Hosted by the International Skin Imaging Collaboration (ISIC)

Reference 69

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no resolver link, observed 2026-08-16T10:42:23.106488Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T10:42:23.106488Z digest=sha256:2f3274fe934fa5883ed95c3dda515b8f0abb50a087bcd74a65c5d09c5df43971

Observation ad878b3f-a99d-4154-8fb5-be3fb20a76bd · outbound

This paper cites Ph 2-a dermoscopic image database for research and benchmarking,.

Mamba-Sea: A Mamba-based Framework with Global-to-Local Sequence Augmentation for Generalizable Medical Image Segmentation Ph 2-a dermoscopic image database for research and benchmarking,

Reference 70

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verified fuzzy
raw_fallback, observed 2026-08-16T10:42:23.508515Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-16T10:42:23.110900Z digest=sha256:f39f3c0a2c9ea17cd91cd48bf1a78aac5d14a0a881c78b7e9cf71d67fbfccf6e

Observation 5f4ad9f1-6097-487e-b169-be426954793d · outbound

This paper cites Sam 2: Segment anything in images and videos,.

Mamba-Sea: A Mamba-based Framework with Global-to-Local Sequence Augmentation for Generalizable Medical Image Segmentation Sam 2: Segment anything in images and videos,

Reference 71

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verified fuzzy
raw_fallback, observed 2026-08-16T10:42:23.494871Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-16T10:42:23.115225Z digest=sha256:545aaff37c0eecb61841c5d37c36e0d7c6b0eddc8ad52106ad0621ae543b753f

Observation 124d5287-c4ff-4f4e-88c3-791adb8e01fa · outbound

This paper cites Sam-adapter: Adapting segment anything in underperformed scenes,.

Mamba-Sea: A Mamba-based Framework with Global-to-Local Sequence Augmentation for Generalizable Medical Image Segmentation Sam-adapter: Adapting segment anything in underperformed scenes,

Reference 72

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T10:42:23.119157Z digest=sha256:49f1fe6c234dccb43474c1df685636516b2b518052e2041ee5f9741e3f170dca

Observation fde4a00d-8c5f-4671-a2a2-6eaeb8847cec · outbound

This paper cites Improving the generalization of segmentation foundation model under distribution shift via weakly supervised adaptation,.

Mamba-Sea: A Mamba-based Framework with Global-to-Local Sequence Augmentation for Generalizable Medical Image Segmentation Improving the generalization of segmentation foundation model under distribution shift via weakly supervised adaptation,

Reference 73

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verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-16T10:42:23.123165Z digest=sha256:359a36cd0af8888e7058f605cee6befadfc3bf01f53d6730e120aa0b470fba43

Observation 7c59c168-c27f-4585-a3cb-506d5e2e20ad · outbound

This paper cites Encoder- decoder with atrous separable convolution for semantic image segmen- tation,.

Mamba-Sea: A Mamba-based Framework with Global-to-Local Sequence Augmentation for Generalizable Medical Image Segmentation Encoder- decoder with atrous separable convolution for semantic image segmen- tation,

Reference 74

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raw_fallback, observed 2026-08-16T10:42:23.458690Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-16T10:42:23.127159Z digest=sha256:0d0da2345830182176c56d7866bee2a571d2f4e18d8caa86edd90c5d22733a68

Observation 55bd2387-5430-4c2c-b3c0-4604a3365260 · outbound

This paper cites Transunet: Rethinking the u-net architecture design for medical image segmentation through the lens of transformers,.

Mamba-Sea: A Mamba-based Framework with Global-to-Local Sequence Augmentation for Generalizable Medical Image Segmentation Transunet: Rethinking the u-net architecture design for medical image segmentation through the lens of transformers,

Reference 75

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verified fuzzy
raw_fallback, observed 2026-08-16T10:42:23.444981Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-16T10:42:23.131351Z digest=sha256:a49bac97c05edf72126d96236e8682c007fd1db3dec3af6d2e06044372a691ca

Observation cfcb2f7c-d83d-4c3a-a723-8905f6a18e9e · outbound

This paper cites Swin transformer: Hierarchical vision transformer using shifted windows,.

Mamba-Sea: A Mamba-based Framework with Global-to-Local Sequence Augmentation for Generalizable Medical Image Segmentation Swin transformer: Hierarchical vision transformer using shifted windows,

Reference 76

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T10:42:23.135468Z digest=sha256:3cf664b070b01222ca0b684e2b7be5e4f014f2eb3abb09dded251d434a9bc92c

Observation 66d9eb28-3edd-4a04-b40e-4ae18e0a0b29 · outbound

This paper cites Transformers are SSMs: Generalized models and efficient algorithms through structured state space duality,.

Mamba-Sea: A Mamba-based Framework with Global-to-Local Sequence Augmentation for Generalizable Medical Image Segmentation Transformers are SSMs: Generalized models and efficient algorithms through structured state space duality,

Reference 77

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T10:42:23.139557Z digest=sha256:c98f53c09780ef563b9cd0dba4e146f205345aa5b741dc6c02a805f248d6b3b1

Observation 2411f205-41e3-44bb-ac0e-35ab91d4f05b · outbound

This paper cites Famba-v: Fast vision mamba with cross-layer token fusion,.

Mamba-Sea: A Mamba-based Framework with Global-to-Local Sequence Augmentation for Generalizable Medical Image Segmentation Famba-v: Fast vision mamba with cross-layer token fusion,

Reference 78

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verified fuzzy
raw_fallback, observed 2026-08-16T10:42:23.411186Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-16T10:42:23.143429Z digest=sha256:bfe4fbf75e36c83617cc999721a95e12efbafff858b53e3b9d47a8c799c14f47

Observation b1880fa9-2211-4adb-9190-6f43a903b9f2 · outbound

This paper cites Segment anything in medical images,.

Mamba-Sea: A Mamba-based Framework with Global-to-Local Sequence Augmentation for Generalizable Medical Image Segmentation Segment anything in medical images,

Reference 79

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no resolver link, observed 2026-08-16T10:42:23.147352Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T10:42:23.147352Z digest=sha256:b152ff040b128a6eb902df150fdb3caa5546d98a8877cc5e1e0cfce8eefb4798

Pith citing papers

No inbound Pith citation observations are available.