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Paper Citation Record · LEDGER

Vision-Language Semantic Aggregation Leveraging Foundation Model for Generalizable Medical Image Segmentation

As of 10 August 2026, this Paper Citation Record lists 42 of 42 outbound references and 0 inbound Pith citation observations for arXiv:2509.08570.

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Outbound references

Observation fb92747d-1b98-4324-90db-40f3a5774590 · outbound

This paper cites A survey of unsupervised deep domain adaptation,.

Vision-Language Semantic Aggregation Leveraging Foundation Model for Generalizable Medical Image Segmentation A survey of unsupervised deep domain adaptation,

Reference 1

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Observation ea9e7f68-f401-4e59-bd1d-7118b4f85780 · outbound

This paper cites A comprehensive survey on source-free domain adaptation,.

Vision-Language Semantic Aggregation Leveraging Foundation Model for Generalizable Medical Image Segmentation A comprehensive survey on source-free domain adaptation,

Reference 2

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This paper cites Domaingeneralizationthroughmeta- learning: a survey,.

Vision-Language Semantic Aggregation Leveraging Foundation Model for Generalizable Medical Image Segmentation Domaingeneralizationthroughmeta- learning: a survey,

Reference 3

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Vision-Language Semantic Aggregation Leveraging Foundation Model for Generalizable Medical Image Segmentation Open domain gen- eralization with domain-augmented meta-learning,

Reference 4

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Observation 6ea44e3f-17de-4d10-9c59-d08bfd9cea35 · outbound

This paper cites Reducing domain gap in frequency and spatial domain for cross-modality domain adaptation on medical image segmentation,.

Vision-Language Semantic Aggregation Leveraging Foundation Model for Generalizable Medical Image Segmentation Reducing domain gap in frequency and spatial domain for cross-modality domain adaptation on medical image segmentation,

Reference 5

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Observation c945286a-b291-4f9b-a1b2-b4b26acd75c0 · outbound

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Vision-Language Semantic Aggregation Leveraging Foundation Model for Generalizable Medical Image Segmentation Domain generaliza- tion: A survey,

Reference 6

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This paper cites Adversarial consistency for single domain generalization in medical image segmentation,.

Vision-Language Semantic Aggregation Leveraging Foundation Model for Generalizable Medical Image Segmentation Adversarial consistency for single domain generalization in medical image segmentation,

Reference 7

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Observation 193d9b5b-5bc5-468d-a211-7f0f1271bec6 · outbound

This paper cites Structure-aware single-source gen- eralization with pixel-level disentanglement for joint optic disc and cup segmentation,.

Vision-Language Semantic Aggregation Leveraging Foundation Model for Generalizable Medical Image Segmentation Structure-aware single-source gen- eralization with pixel-level disentanglement for joint optic disc and cup segmentation,

Reference 8

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This paper cites Multi- receptive field feature disentanglement with distance-aware gaussian brightness augmentation for single-source domain generalization in med- ical image segmentation,.

Vision-Language Semantic Aggregation Leveraging Foundation Model for Generalizable Medical Image Segmentation Multi- receptive field feature disentanglement with distance-aware gaussian brightness augmentation for single-source domain generalization in med- ical image segmentation,

Reference 9

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This paper cites Attention is all you need,.

Vision-Language Semantic Aggregation Leveraging Foundation Model for Generalizable Medical Image Segmentation Attention is all you need,

Reference 10

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Observation 9e51322c-0d8a-4cda-a2dd-1bd893f7c85a · outbound

This paper cites Learningtransferablevisualmodelsfromnaturallanguagesupervision,.

Vision-Language Semantic Aggregation Leveraging Foundation Model for Generalizable Medical Image Segmentation Learningtransferablevisualmodelsfromnaturallanguagesupervision,

Reference 11

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This paper cites Textual query-driven mask transformer for domain generalized segmentation,.

Vision-Language Semantic Aggregation Leveraging Foundation Model for Generalizable Medical Image Segmentation Textual query-driven mask transformer for domain generalized segmentation,

Reference 12

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Observation 90db9ef3-2e6c-422b-b98e-981035eefd2a · outbound

This paper cites Uni- fied contrastive learning in image-text-label space,.

Vision-Language Semantic Aggregation Leveraging Foundation Model for Generalizable Medical Image Segmentation Uni- fied contrastive learning in image-text-label space,

Reference 13

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Vision-Language Semantic Aggregation Leveraging Foundation Model for Generalizable Medical Image Segmentation Pyramidclip: Hierarchical feature alignment for vision-language model pretraining,

Reference 14

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Vision-Language Semantic Aggregation Leveraging Foundation Model for Generalizable Medical Image Segmentation Dinov2: Learning robust visual features without supervision,

Reference 15

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Observation 195c45ec-0f79-4eb7-8654-bac8d07a1d69 · outbound

This paper cites Stronger fewer & superior: Harnessing vision foundation models for domain generalized semantic segmentation,.

Vision-Language Semantic Aggregation Leveraging Foundation Model for Generalizable Medical Image Segmentation Stronger fewer & superior: Harnessing vision foundation models for domain generalized semantic segmentation,

Reference 16

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Observation cebbf11e-1774-43b1-8f9f-dcb8a834a362 · outbound

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Vision-Language Semantic Aggregation Leveraging Foundation Model for Generalizable Medical Image Segmentation Causality-inspired single-source domain generalization for medical im- age segmentation,

Reference 17

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Observation 78559ec5-8764-4a23-8bc6-3433b1d1877e · outbound

This paper cites Devil is in channels: Contrastive single domain generalization for medical image segmentation,.

Vision-Language Semantic Aggregation Leveraging Foundation Model for Generalizable Medical Image Segmentation Devil is in channels: Contrastive single domain generalization for medical image segmentation,

Reference 18

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Vision-Language Semantic Aggregation Leveraging Foundation Model for Generalizable Medical Image Segmentation Single domain generalization for multimodal cross-cancer prognosis via dirac rebalancer and distribution entanglement,

Reference 19

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This paper cites Denseclip: Language-guided dense prediction with context-aware 26 prompting,.

Vision-Language Semantic Aggregation Leveraging Foundation Model for Generalizable Medical Image Segmentation Denseclip: Language-guided dense prediction with context-aware 26 prompting,

Reference 20

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Vision-Language Semantic Aggregation Leveraging Foundation Model for Generalizable Medical Image Segmentation Maximum likelihood from incomplete data via the em algorithm,

Reference 21

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Vision-Language Semantic Aggregation Leveraging Foundation Model for Generalizable Medical Image Segmentation Mamba as a bridge: Where vision foun- dation models meet vision language models for domain-generalized se- mantic segmentation,

Reference 22

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Vision-Language Semantic Aggregation Leveraging Foundation Model for Generalizable Medical Image Segmentation Acceleration of the em algorithm by using quasi-newton methods,

Reference 23

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Vision-Language Semantic Aggregation Leveraging Foundation Model for Generalizable Medical Image Segmentation Prompt tuning for parameter- efficient medical image segmentation,

Reference 24

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Vision-Language Semantic Aggregation Leveraging Foundation Model for Generalizable Medical Image Segmentation Cat: Coordi- nating anatomical-textual prompts for multi-organ and tumor segmen- tation,

Reference 25

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Vision-Language Semantic Aggregation Leveraging Foundation Model for Generalizable Medical Image Segmentation Multi-modality cross attention network for image and sentence matching,

Reference 26

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Vision-Language Semantic Aggregation Leveraging Foundation Model for Generalizable Medical Image Segmentation Hierarchical self-attention network for industrial data series modeling with different sampling rates between the input and output sequences,

Reference 27

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Vision-Language Semantic Aggregation Leveraging Foundation Model for Generalizable Medical Image Segmentation Transformers in vision: A survey,

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Vision-Language Semantic Aggregation Leveraging Foundation Model for Generalizable Medical Image Segmentation V-net: Fully convolutional neural networks for volumetric medical image segmentation,

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Vision-Language Semantic Aggregation Leveraging Foundation Model for Generalizable Medical Image Segmentation Learning to prompt for vision- language models,

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Vision-Language Semantic Aggregation Leveraging Foundation Model for Generalizable Medical Image Segmentation A similarity paradigm through textual regularization without forgetting,

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Vision-Language Semantic Aggregation Leveraging Foundation Model for Generalizable Medical Image Segmentation Adam: A Method for Stochastic Optimization

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This paper cites Deep learning techniques for automatic mri cardiac multi-structures segmen- tation and diagnosis: Is the problem solved?,.

Vision-Language Semantic Aggregation Leveraging Foundation Model for Generalizable Medical Image Segmentation Deep learning techniques for automatic mri cardiac multi-structures segmen- tation and diagnosis: Is the problem solved?,

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This paper cites FairDomain: Achieving Fairness in Cross-Domain Medical Image Segmentation and Classification.

Vision-Language Semantic Aggregation Leveraging Foundation Model for Generalizable Medical Image Segmentation FairDomain: Achieving Fairness in Cross-Domain Medical Image Segmentation and Classification

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Vision-Language Semantic Aggregation Leveraging Foundation Model for Generalizable Medical Image Segmentation U-net: Convolutional net- works for biomedical image segmentation,

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Observation 8977c82e-621c-4fc5-82a9-c666e5a62490 · outbound

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Vision-Language Semantic Aggregation Leveraging Foundation Model for Generalizable Medical Image Segmentation H2former: An efficient hierarchical hybrid transformer for medical image segmentation,

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Observation 28470aca-8529-4dfc-b563-1df3484453e8 · outbound

This paper cites Improved regularization of convolutional neu- ral networks with cutout,.

Vision-Language Semantic Aggregation Leveraging Foundation Model for Generalizable Medical Image Segmentation Improved regularization of convolutional neu- ral networks with cutout,

Reference 37

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Observation d26f7384-3ef9-4e44-b2c0-19f09b61d903 · outbound

This paper cites Robust and generalizable visual representation learning via random convolutions,.

Vision-Language Semantic Aggregation Leveraging Foundation Model for Generalizable Medical Image Segmentation Robust and generalizable visual representation learning via random convolutions,

Reference 38

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Observation 592ef65e-deec-4652-acd0-bd4cddcb5689 · outbound

This paper cites Domain generalization with mixstyle,.

Vision-Language Semantic Aggregation Leveraging Foundation Model for Generalizable Medical Image Segmentation Domain generalization with mixstyle,

Reference 39

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Observation cae84a33-56ab-484b-bbf6-5195977fb3cd · outbound

This paper cites Rethinking data augmentation for single-source domain generalization in medical image segmentation,.

Vision-Language Semantic Aggregation Leveraging Foundation Model for Generalizable Medical Image Segmentation Rethinking data augmentation for single-source domain generalization in medical image segmentation,

Reference 40

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Observation 21c65fa8-8146-4d99-90c8-3ec2890f33d1 · outbound

This paper cites Prompting seg- ment anything model with domain-adaptive prototype for generalizable medical image segmentation,.

Vision-Language Semantic Aggregation Leveraging Foundation Model for Generalizable Medical Image Segmentation Prompting seg- ment anything model with domain-adaptive prototype for generalizable medical image segmentation,

Reference 41

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Observation 3110ab47-5f24-4d2c-967b-39c8dc1984a9 · outbound

This paper cites A theory of learning from different domains,.

Vision-Language Semantic Aggregation Leveraging Foundation Model for Generalizable Medical Image Segmentation A theory of learning from different domains,

Reference 42

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