Paper Citation Record · LEDGER

J-CaPA : Joint Channel and Pyramid Attention Improves Medical Image Segmentation

As of 14 August 2026, this Paper Citation Record lists 41 of 41 outbound references and 1 inbound Pith citation observation for arXiv:2411.16568.

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

pith.paper-citation-record.v1
2411.16568 v1

Coverage vector

measured 41 of 41 reference resolution

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measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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Source: pith, observed 2026-08-12T13:01:46.832030Z

Reference resolution

41 of 41 outbound references displayed

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External citation measurements

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Outbound references

Observation 92a7c8e0-047f-46b0-9411-41350bea71fe · outbound

This paper cites However, it remains challenging due to the varying size, shape, and appearance of different organs and pathologies.

J-CaPA : Joint Channel and Pyramid Attention Improves Medical Image Segmentation However, it remains challenging due to the varying size, shape, and appearance of different organs and pathologies

Reference 1

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Observation c5ff8c9e-3b08-4a24-8ab9-c277589d2d89 · outbound

This paper cites CNN-Based Methods for Medical Image Segmenta- tion CNNs, including FCNs [5] and U-Net variants [1], have shown strong segmentation performance.

J-CaPA : Joint Channel and Pyramid Attention Improves Medical Image Segmentation CNN-Based Methods for Medical Image Segmenta- tion CNNs, including FCNs [5] and U-Net variants [1], have shown strong segmentation performance

Reference 2

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Observation 92e7ce3c-ee06-44e3-abe1-0abaa4115bef · outbound

This paper cites Model Architecture Overview The overall architecture of our model is a Transformer based structure, with an encoder-decoder design as shown in Fig- ure 1.

J-CaPA : Joint Channel and Pyramid Attention Improves Medical Image Segmentation Model Architecture Overview The overall architecture of our model is a Transformer based structure, with an encoder-decoder design as shown in Fig- ure 1

Reference 3

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Observation d85fd989-d8d5-426d-8184-74dea5ea1746 · outbound

This paper cites Results are provided in Table 1.

J-CaPA : Joint Channel and Pyramid Attention Improves Medical Image Segmentation Results are provided in Table 1

Reference 4

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Observation 56007313-8318-41ea-b7c1-f8eb8d94e2aa · outbound

This paper cites The results, shown in Table 2, compare a baseline implementation without our en- hancements to the model with each enhancement added.

J-CaPA : Joint Channel and Pyramid Attention Improves Medical Image Segmentation The results, shown in Table 2, compare a baseline implementation without our en- hancements to the model with each enhancement added

Reference 5

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Observation ed3f8765-b5ca-4f7d-a809-97b3895fda8d · outbound

This paper cites Our approach demonstrates notable im- provements in segmentation accuracy and generalization, par- ticularly for challenging organs in abdominal CT scans.

J-CaPA : Joint Channel and Pyramid Attention Improves Medical Image Segmentation Our approach demonstrates notable im- provements in segmentation accuracy and generalization, par- ticularly for challenging organs in abdominal CT scans

Reference 6

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This paper cites No further ethical approval was required.

J-CaPA : Joint Channel and Pyramid Attention Improves Medical Image Segmentation No further ethical approval was required

Reference 7

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This paper cites Yuyin Zhou for her guidance and sug- gestions throughout the duration of this project, and Vanshika Vats for her valuable feedback.

J-CaPA : Joint Channel and Pyramid Attention Improves Medical Image Segmentation Yuyin Zhou for her guidance and sug- gestions throughout the duration of this project, and Vanshika Vats for her valuable feedback

Reference 8

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Observation 026ebd11-81c9-4495-b4b2-f5bfb0a991ac · outbound

This paper cites J-CaPA : Joint Channel and Pyramid Attention Improves Medical Image Segmentation.

J-CaPA : Joint Channel and Pyramid Attention Improves Medical Image Segmentation J-CaPA : Joint Channel and Pyramid Attention Improves Medical Image Segmentation

Reference 9

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Observation 1954f5ea-906e-49ef-98e8-12ad0e5456d6 · outbound

This paper cites U-Net: Convo- lutional Networks for Biomedical Image Segmentation,.

J-CaPA : Joint Channel and Pyramid Attention Improves Medical Image Segmentation U-Net: Convo- lutional Networks for Biomedical Image Segmentation,

Reference 10

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This paper cites KiU-Net: Accurate Segmentation of Biomedical Im- ages using Over-complete Representations,.

J-CaPA : Joint Channel and Pyramid Attention Improves Medical Image Segmentation KiU-Net: Accurate Segmentation of Biomedical Im- ages using Over-complete Representations,

Reference 11

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This paper cites Weighted Res-UNet for High-Quality Retina Vessel Segmentation,.

J-CaPA : Joint Channel and Pyramid Attention Improves Medical Image Segmentation Weighted Res-UNet for High-Quality Retina Vessel Segmentation,

Reference 12

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Observation 0866dcfd-22d1-4b7e-9162-c6ad31c694f6 · outbound

This paper cites Recurrent Residual Convolutional Neural Network based on U-Net (R2U-Net) for Medical Image Segmentation.

J-CaPA : Joint Channel and Pyramid Attention Improves Medical Image Segmentation Recurrent Residual Convolutional Neural Network based on U-Net (R2U-Net) for Medical Image Segmentation

Reference 13

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This paper cites Fully Convolutional Networks for Semantic Segmentation,.

J-CaPA : Joint Channel and Pyramid Attention Improves Medical Image Segmentation Fully Convolutional Networks for Semantic Segmentation,

Reference 14

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Observation ba7dc756-2c1b-4125-afe6-392803035060 · outbound

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

J-CaPA : Joint Channel and Pyramid Attention Improves Medical Image Segmentation UNet++: A Nested U-Net Architecture for Medical Image Segmentation,

Reference 15

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This paper cites Attention U-Net: Learning Where to Look for the Pancreas.

J-CaPA : Joint Channel and Pyramid Attention Improves Medical Image Segmentation Attention U-Net: Learning Where to Look for the Pancreas

Reference 16

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This paper cites DoubleU-Net: A Deep Convolutional Neural Net- work for Medical Image Segmentation,.

J-CaPA : Joint Channel and Pyramid Attention Improves Medical Image Segmentation DoubleU-Net: A Deep Convolutional Neural Net- work for Medical Image Segmentation,

Reference 17

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This paper cites PraNet: Parallel Reverse Attention Network for Polyp Segmentation,.

J-CaPA : Joint Channel and Pyramid Attention Improves Medical Image Segmentation PraNet: Parallel Reverse Attention Network for Polyp Segmentation,

Reference 18

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Observation ced362d6-f60c-47ea-ad35-9a748fe32b75 · outbound

This paper cites Non-local Neural Networks,.

J-CaPA : Joint Channel and Pyramid Attention Improves Medical Image Segmentation Non-local Neural Networks,

Reference 19

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Observation 7792ddb2-0dae-4ad9-b9d8-e1859bfc6273 · outbound

This paper cites Attention Gated Networks: Learning to Leverage Salient Regions in Medical Images,.

J-CaPA : Joint Channel and Pyramid Attention Improves Medical Image Segmentation Attention Gated Networks: Learning to Leverage Salient Regions in Medical Images,

Reference 20

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Observation 1d25235c-7c99-42a8-b58d-415baea1ff22 · outbound

This paper cites Attention Is All You Need,.

J-CaPA : Joint Channel and Pyramid Attention Improves Medical Image Segmentation Attention Is All You Need,

Reference 21

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Observation f6b401df-af6a-4e1a-bcd8-0e3249dcf32a · outbound

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

J-CaPA : Joint Channel and Pyramid Attention Improves Medical Image Segmentation An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 22

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

J-CaPA : Joint Channel and Pyramid Attention Improves Medical Image Segmentation TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation

Reference 23

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This paper cites Swin-Unet: Unet-like Pure Transformer for Med- ical Image Segmentation,.

J-CaPA : Joint Channel and Pyramid Attention Improves Medical Image Segmentation Swin-Unet: Unet-like Pure Transformer for Med- ical Image Segmentation,

Reference 24

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This paper cites DS- TransUNet: Dual Swin Transformer U-Net for Medical Image Segmentation,.

J-CaPA : Joint Channel and Pyramid Attention Improves Medical Image Segmentation DS- TransUNet: Dual Swin Transformer U-Net for Medical Image Segmentation,

Reference 25

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This paper cites AA-TransUNet: Attention Aug- mented TransUNet For Nowcasting Tasks,.

J-CaPA : Joint Channel and Pyramid Attention Improves Medical Image Segmentation AA-TransUNet: Attention Aug- mented TransUNet For Nowcasting Tasks,

Reference 26

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Observation ef1c8d42-d5d2-46c7-81b1-ace3eaae2ca7 · outbound

This paper cites DA-TransUNet: Integrating Spatial and Channel Dual Attention with Transformer U-Net for Medical Image Segmentation,.

J-CaPA : Joint Channel and Pyramid Attention Improves Medical Image Segmentation DA-TransUNet: Integrating Spatial and Channel Dual Attention with Transformer U-Net for Medical Image Segmentation,

Reference 27

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Observation 5acae406-b5a4-4c70-b4bb-5e0f9c2f41ea · outbound

This paper cites Segment Anything,.

J-CaPA : Joint Channel and Pyramid Attention Improves Medical Image Segmentation Segment Anything,

Reference 28

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Observation 4d33139c-98a8-4734-bb21-a96c82ac1ae7 · outbound

This paper cites SegGPT: Towards Segmenting Everything in Context,.

J-CaPA : Joint Channel and Pyramid Attention Improves Medical Image Segmentation SegGPT: Towards Segmenting Everything in Context,

Reference 29

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This paper cites STU-Net: Scalable and Transferable Medical Image Segmentation Models Empowered by Large-Scale Supervised Pre-training.

J-CaPA : Joint Channel and Pyramid Attention Improves Medical Image Segmentation STU-Net: Scalable and Transferable Medical Image Segmentation Models Empowered by Large-Scale Supervised Pre-training

Reference 30

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This paper cites Segment Anything in Medical Images,.

J-CaPA : Joint Channel and Pyramid Attention Improves Medical Image Segmentation Segment Anything in Medical Images,

Reference 31

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Observation d7e2b30b-80e8-4279-8f9d-3f1532c246bc · outbound

This paper cites Segment Any- thing Model for Medical Image Segmentation: Current ap- plications and future directions,.

J-CaPA : Joint Channel and Pyramid Attention Improves Medical Image Segmentation Segment Any- thing Model for Medical Image Segmentation: Current ap- plications and future directions,

Reference 32

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Observation 01f83b4c-fda3-43c4-b385-8d52256b30e2 · outbound

This paper cites How to Efficiently Adapt Large Segmentation Model(SAM) to Medical Images.

J-CaPA : Joint Channel and Pyramid Attention Improves Medical Image Segmentation How to Efficiently Adapt Large Segmentation Model(SAM) to Medical Images

Reference 33

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J-CaPA : Joint Channel and Pyramid Attention Improves Medical Image Segmentation Customized Segment Anything Model for Medical Image Segmentation

Reference 34

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

J-CaPA : Joint Channel and Pyramid Attention Improves Medical Image Segmentation LoRA: Low-Rank Adaptation of Large Language Models

Reference 35

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Observation 43f2c4c8-9633-41c3-8c2f-6e52d5329186 · outbound

This paper cites TransNorm: Transformer Provides a Strong Spatial Normal- ization Mechanism for a Deep Segmentation Model,.

J-CaPA : Joint Channel and Pyramid Attention Improves Medical Image Segmentation TransNorm: Transformer Provides a Strong Spatial Normal- ization Mechanism for a Deep Segmentation Model,

Reference 36

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Observation efb2d791-01fe-4d93-8c0b-e4c33f9a8a32 · outbound

This paper cites IB-TransUNet: Combin- ing Information Bottleneck and Transformer for Medical Im- age Segmentation,.

J-CaPA : Joint Channel and Pyramid Attention Improves Medical Image Segmentation IB-TransUNet: Combin- ing Information Bottleneck and Transformer for Medical Im- age Segmentation,

Reference 37

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This paper cites Dual Attention Network for Scene Segmentation,.

J-CaPA : Joint Channel and Pyramid Attention Improves Medical Image Segmentation Dual Attention Network for Scene Segmentation,

Reference 38

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Observation 91f4f0ae-fe82-490d-bb3a-e5a8a015779d · outbound

This paper cites TA-Net: Triple Attention Network for Medical Image Segmentation,.

J-CaPA : Joint Channel and Pyramid Attention Improves Medical Image Segmentation TA-Net: Triple Attention Network for Medical Image Segmentation,

Reference 39

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Observation 1a79f857-09a3-4f95-9af9-44c34fd4feb2 · outbound

This paper cites A Multi- Class COVID-19 Segmentation Network with Pyramid Atten- tion and Edge Loss in CT Images,.

J-CaPA : Joint Channel and Pyramid Attention Improves Medical Image Segmentation A Multi- Class COVID-19 Segmentation Network with Pyramid Atten- tion and Edge Loss in CT Images,

Reference 40

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Observation 611e202f-e2b8-46fa-879d-2b7c4fef3613 · outbound

This paper cites Cut- Mix: Regularization Strategy to Train Strong Classifiers With Localizable Features,.

J-CaPA : Joint Channel and Pyramid Attention Improves Medical Image Segmentation Cut- Mix: Regularization Strategy to Train Strong Classifiers With Localizable Features,

Reference 41

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Pith citing papers

Observation 026ebd11-81c9-4495-b4b2-f5bfb0a991ac · inbound