Paper Citation Record · LEDGER

MSA2-Net: Utilizing Self-Adaptive Convolution Module to Extract Multi-Scale Information in Medical Image Segmentation

As of 16 August 2026, this Paper Citation Record lists 39 of 39 outbound references and 0 inbound Pith citation observations for arXiv:2509.01498.

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

pith.paper-citation-record.v1
2509.01498 v2

Coverage vector

measured 39 of 39 reference resolution

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One-hop event checks from named stored sources.

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measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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

Source: cited_works

Reference resolution

39 of 39 outbound references displayed

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External citation measurements

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Outbound references

Observation 49c33964-74aa-47e0-b377-b7aaed3c84fd · outbound

This paper cites Deep semantic segmentation of natural and medical im- ages: a review,.

MSA2-Net: Utilizing Self-Adaptive Convolution Module to Extract Multi-Scale Information in Medical Image Segmentation Deep semantic segmentation of natural and medical im- ages: a review,

Reference 1

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Observation 039b97bd-a724-4bd6-8131-60ed156e61c8 · outbound

This paper cites Medical image segmentation using deep semantic- based methods: A review of techniques, applications and emerging trends,.

MSA2-Net: Utilizing Self-Adaptive Convolution Module to Extract Multi-Scale Information in Medical Image Segmentation Medical image segmentation using deep semantic- based methods: A review of techniques, applications and emerging trends,

Reference 2

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Observation d14dd6f1-e7f6-4c6e-8274-0795f55040c7 · outbound

This paper cites Medical image segmentation using deep learning: A survey,.

MSA2-Net: Utilizing Self-Adaptive Convolution Module to Extract Multi-Scale Information in Medical Image Segmentation Medical image segmentation using deep learning: A survey,

Reference 3

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Observation e40fd497-7657-46b6-8852-24c6861157ba · outbound

This paper cites Fully convolutional networks for semantic segmentation,.

MSA2-Net: Utilizing Self-Adaptive Convolution Module to Extract Multi-Scale Information in Medical Image Segmentation Fully convolutional networks for semantic segmentation,

Reference 4

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Observation 2a456027-07a4-4241-82f6-be1fcdb8e036 · outbound

This paper cites On the texture bias for few-shot cnn segmentation,.

MSA2-Net: Utilizing Self-Adaptive Convolution Module to Extract Multi-Scale Information in Medical Image Segmentation On the texture bias for few-shot cnn segmentation,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:33:59.901692Z

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Observation c2335b8b-b865-4f41-9d82-66c727de5adb · outbound

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

MSA2-Net: Utilizing Self-Adaptive Convolution Module to Extract Multi-Scale Information in Medical Image Segmentation TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation

Reference 6

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Observation ddb525df-d3cd-4f21-ac09-acdbdd95ac64 · outbound

This paper cites Short- term and long-term memory self-attention network for segmentation of tumours in 3d medical images,.

MSA2-Net: Utilizing Self-Adaptive Convolution Module to Extract Multi-Scale Information in Medical Image Segmentation Short- term and long-term memory self-attention network for segmentation of tumours in 3d medical images,

Reference 7

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Observation 24d22b1d-7d8b-4d31-8c1a-87bde9745855 · outbound

This paper cites MISSFormer: An Effective Medical Image Segmentation Transformer.

MSA2-Net: Utilizing Self-Adaptive Convolution Module to Extract Multi-Scale Information in Medical Image Segmentation MISSFormer: An Effective Medical Image Segmentation Transformer

Reference 8

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Observation 4d8fc2f4-96fb-461e-ba9f-17b89e73af42 · outbound

This paper cites Disegnet: A deep dilated convolutional encoder-decoder architecture for lymph node segmentation on pet/ct images,.

MSA2-Net: Utilizing Self-Adaptive Convolution Module to Extract Multi-Scale Information in Medical Image Segmentation Disegnet: A deep dilated convolutional encoder-decoder architecture for lymph node segmentation on pet/ct images,

Reference 9

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Observation 50f3e085-094e-47ce-9575-fa77e7b159ca · outbound

This paper cites Sca-cnn: Spatial and channel-wise attention in convolutional networks for image captioning,.

MSA2-Net: Utilizing Self-Adaptive Convolution Module to Extract Multi-Scale Information in Medical Image Segmentation Sca-cnn: Spatial and channel-wise attention in convolutional networks for image captioning,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:33:59.380179Z

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Observation 3b4de768-bc22-4823-82ee-c73a02b4c35d · outbound

This paper cites Adaptive spatial pixel-level feature fusion network for multispectral pedestrian detection,.

MSA2-Net: Utilizing Self-Adaptive Convolution Module to Extract Multi-Scale Information in Medical Image Segmentation Adaptive spatial pixel-level feature fusion network for multispectral pedestrian detection,

Reference 11

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Observation d0aa8f35-8350-4f34-ad33-6a7c44d7afd4 · outbound

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

MSA2-Net: Utilizing Self-Adaptive Convolution Module to Extract Multi-Scale Information in Medical Image Segmentation nnu-net: a self-configuring method for deep learning-based biomedical image segmentation,

Reference 12

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Observation fb03b5e6-d1bf-46d7-9a7e-d677fdd5bc43 · outbound

This paper cites Cswin transformer: A general vision transformer backbone with cross-shaped windows,.

MSA2-Net: Utilizing Self-Adaptive Convolution Module to Extract Multi-Scale Information in Medical Image Segmentation Cswin transformer: A general vision transformer backbone with cross-shaped windows,

Reference 13

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Observation 26afbef3-aab1-4bdd-97f0-d8bc008b74b6 · outbound

This paper cites Unet++: A nested u-net architecture for medical image segmentation,.

MSA2-Net: Utilizing Self-Adaptive Convolution Module to Extract Multi-Scale Information in Medical Image Segmentation Unet++: A nested u-net architecture for medical image segmentation,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:33:58.695986Z

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Observation b67f4a9a-4323-4b65-a05f-25c4bbe13908 · outbound

This paper cites Age estimation from mr images via 3d convolutional neural network and densely connect,.

MSA2-Net: Utilizing Self-Adaptive Convolution Module to Extract Multi-Scale Information in Medical Image Segmentation Age estimation from mr images via 3d convolutional neural network and densely connect,

Reference 15

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Observation c3261885-616c-4c96-ba59-561827397045 · outbound

This paper cites Do- main adaptive relational reasoning for 3d multi-organ segmentation,.

MSA2-Net: Utilizing Self-Adaptive Convolution Module to Extract Multi-Scale Information in Medical Image Segmentation Do- main adaptive relational reasoning for 3d multi-organ segmentation,

Reference 16

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Observation 325c6f5f-6ae7-4758-b951-cb1b092f4a5d · outbound

This paper cites Medical image segmentation via cascaded attention decoding,.

MSA2-Net: Utilizing Self-Adaptive Convolution Module to Extract Multi-Scale Information in Medical Image Segmentation Medical image segmentation via cascaded attention decoding,

Reference 17

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Observation 4223f321-cd51-4acd-9f8e-1d37cc1e2bcd · outbound

This paper cites TransResU-Net: Transformer based ResU-Net for Real-Time Colonoscopy Polyp Segmentation.

MSA2-Net: Utilizing Self-Adaptive Convolution Module to Extract Multi-Scale Information in Medical Image Segmentation TransResU-Net: Transformer based ResU-Net for Real-Time Colonoscopy Polyp Segmentation

Reference 18

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Observation 215e8daf-44d8-40ef-9786-b65b0496360f · outbound

This paper cites Hiformer: Hierarchical multi-scale representations using transformers for medical image segmentation,.

MSA2-Net: Utilizing Self-Adaptive Convolution Module to Extract Multi-Scale Information in Medical Image Segmentation Hiformer: Hierarchical multi-scale representations using transformers for medical image segmentation,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:33:57.413287Z

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Observation d93456c1-6f3c-436b-b67b-af6a031cdcfa · outbound

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

MSA2-Net: Utilizing Self-Adaptive Convolution Module to Extract Multi-Scale Information in Medical Image Segmentation U-net: Convolutional networks for biomedical image segmentation,

Reference 20

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Observation fc2d9780-8e8a-4ffc-b952-85295ba58990 · outbound

This paper cites Att-unet: Pixel-wise staircase attention for weed and crop detection,.

MSA2-Net: Utilizing Self-Adaptive Convolution Module to Extract Multi-Scale Information in Medical Image Segmentation Att-unet: Pixel-wise staircase attention for weed and crop detection,

Reference 21

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Observation 668c4336-6a27-43f3-ac66-58900f849f8d · outbound

This paper cites Stepwise feature fusion: Local guides global,.

MSA2-Net: Utilizing Self-Adaptive Convolution Module to Extract Multi-Scale Information in Medical Image Segmentation Stepwise feature fusion: Local guides global,

Reference 22

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Observation 8aa5bc2b-196a-4cb3-a464-b09dab922cb6 · outbound

This paper cites Polyp-PVT: Polyp Segmentation with Pyramid Vision Transformers.

MSA2-Net: Utilizing Self-Adaptive Convolution Module to Extract Multi-Scale Information in Medical Image Segmentation Polyp-PVT: Polyp Segmentation with Pyramid Vision Transformers

Reference 23

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This paper cites Mixed transformer u-net for medical image segmentation,.

MSA2-Net: Utilizing Self-Adaptive Convolution Module to Extract Multi-Scale Information in Medical Image Segmentation Mixed transformer u-net for medical image segmentation,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:33:56.654279Z

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Observation 32783f18-44ee-4189-9fa6-ba746f88797f · outbound

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

MSA2-Net: Utilizing Self-Adaptive Convolution Module to Extract Multi-Scale Information in Medical Image Segmentation Swin-unet: Unet-like pure transformer for medical image segmentation,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:33:56.480816Z

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Observation 96c93a04-10e5-4c98-bc30-40591d3b43fc · outbound

This paper cites Class-aware adversarial transformers for medical image seg- mentation,.

MSA2-Net: Utilizing Self-Adaptive Convolution Module to Extract Multi-Scale Information in Medical Image Segmentation Class-aware adversarial transformers for medical image seg- mentation,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:33:56.250005Z

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Observation 5541afa4-1d8c-441d-bfc5-a5584c86dc3b · outbound

This paper cites Bdg-net: boundary distribution guided network for accurate polyp segmentation,.

MSA2-Net: Utilizing Self-Adaptive Convolution Module to Extract Multi-Scale Information in Medical Image Segmentation Bdg-net: boundary distribution guided network for accurate polyp segmentation,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:33:56.057537Z

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Observation 4b90a111-6a79-4c0b-bc0f-5d7a910b1f83 · outbound

This paper cites Identify- ing weaknesses for chilean e-government implementation in public agen- cies with maturity model,.

MSA2-Net: Utilizing Self-Adaptive Convolution Module to Extract Multi-Scale Information in Medical Image Segmentation Identify- ing weaknesses for chilean e-government implementation in public agen- cies with maturity model,

Reference 28

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Observation 8c08f7f1-194f-4036-93a9-3709c11c4e5e · outbound

This paper cites U-net++ dsm: improved u-net++ for brain tumor segmen- tation with deep supervision mechanism,.

MSA2-Net: Utilizing Self-Adaptive Convolution Module to Extract Multi-Scale Information in Medical Image Segmentation U-net++ dsm: improved u-net++ for brain tumor segmen- tation with deep supervision mechanism,

Reference 29

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Observation fed92d37-ff18-4ddb-ad8b-1af5f30d495a · outbound

This paper cites Unetr: Transformers for 3d medical image segmentation,.

MSA2-Net: Utilizing Self-Adaptive Convolution Module to Extract Multi-Scale Information in Medical Image Segmentation Unetr: Transformers for 3d medical image segmentation,

Reference 30

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Observation 0a35d6b7-9b6b-4553-aced-f41d855d8343 · outbound

This paper cites Polyp-SAM++: Can A Text Guided SAM Perform Better for Polyp Segmentation?.

MSA2-Net: Utilizing Self-Adaptive Convolution Module to Extract Multi-Scale Information in Medical Image Segmentation Polyp-SAM++: Can A Text Guided SAM Perform Better for Polyp Segmentation?

Reference 31

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Observation 1c8b7521-f0d4-4775-9d58-00d3347576ab · outbound

This paper cites Connecting targets via latent topics and contrastive learning: A unified framework for robust zero-shot and few-shot stance detection,.

MSA2-Net: Utilizing Self-Adaptive Convolution Module to Extract Multi-Scale Information in Medical Image Segmentation Connecting targets via latent topics and contrastive learning: A unified framework for robust zero-shot and few-shot stance detection,

Reference 32

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Observation 9be62cee-2f13-4974-9d7f-85a70e7eb6b4 · outbound

This paper cites Cswin-unet: Transformer unet with cross-shaped windows for medical image segmentation,.

MSA2-Net: Utilizing Self-Adaptive Convolution Module to Extract Multi-Scale Information in Medical Image Segmentation Cswin-unet: Transformer unet with cross-shaped windows for medical image segmentation,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:33:55.193452Z

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Observation 8dc29c2b-8924-476c-8883-f1aed64ca681 · outbound

This paper cites Pefnet: Position enhancement faster network for object detection in roadside perception system,.

MSA2-Net: Utilizing Self-Adaptive Convolution Module to Extract Multi-Scale Information in Medical Image Segmentation Pefnet: Position enhancement faster network for object detection in roadside perception system,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:33:54.908887Z

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Observation a4b1d64e-4eb6-4656-94e7-eccdb06e5e25 · outbound

This paper cites ST-UNet: A Spatio-Temporal U-Network for Graph-structured Time Series Modeling.

MSA2-Net: Utilizing Self-Adaptive Convolution Module to Extract Multi-Scale Information in Medical Image Segmentation ST-UNet: A Spatio-Temporal U-Network for Graph-structured Time Series Modeling

Reference 35

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Observation eb5d8d8b-c1ab-4048-97a9-f62580ca9ecf · outbound

This paper cites Transnetr: transformer- based residual network for polyp segmentation with multi-center out-of- distribution testing,.

MSA2-Net: Utilizing Self-Adaptive Convolution Module to Extract Multi-Scale Information in Medical Image Segmentation Transnetr: transformer- based residual network for polyp segmentation with multi-center out-of- distribution testing,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:33:54.774486Z

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Observation 0cced2ac-37fe-4715-af30-7eed3330882d · outbound

This paper cites Unetformer: A unet-like transformer for efficient semantic segmentation of remote sensing urban scene imagery,.

MSA2-Net: Utilizing Self-Adaptive Convolution Module to Extract Multi-Scale Information in Medical Image Segmentation Unetformer: A unet-like transformer for efficient semantic segmentation of remote sensing urban scene imagery,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:33:54.598677Z

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Observation f3106879-e031-458c-8aa5-28eed975b2d5 · outbound

This paper cites Resunet++: An advanced architecture for medical image segmentation,.

MSA2-Net: Utilizing Self-Adaptive Convolution Module to Extract Multi-Scale Information in Medical Image Segmentation Resunet++: An advanced architecture for medical image segmentation,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:33:54.442736Z

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Observation be2250d9-6f70-4709-9da9-8e1ff9037381 · outbound

This paper cites Contextual Attention Network: Transformer Meets U-Net.

MSA2-Net: Utilizing Self-Adaptive Convolution Module to Extract Multi-Scale Information in Medical Image Segmentation Contextual Attention Network: Transformer Meets U-Net

Reference 39

Resolution: verified exact; local_arxiv, observed 2026-08-05T12:33:53.983576Z

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

Pith citing papers

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