Leveraging Segment Anything Model for Source-Free Domain Adaptation via Dual Feature Guided Auto-Prompting | Paper Citation Record

Observation 2d9815a2-3d33-4a91-b480-dfcf7b6d9f51 · outbound

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

Leveraging Segment Anything Model for Source-Free Domain Adaptation via Dual Feature Guided Auto-Prompting U-net: Convolutional networks for biomedical image segmentation,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:57:53.785793Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation e5413d57-7419-46a6-ada8-0f554d704570 · outbound

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

Leveraging Segment Anything Model for Source-Free Domain Adaptation via Dual Feature Guided Auto-Prompting Medical image segmentation using deep learning: A survey,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:57:53.773101Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 3bea771c-06f3-495b-a5ec-aadeae5250a2 · outbound

This paper cites A-eval: A benchmark for cross-dataset and cross- modality evaluation of abdominal multi-organ segmentation,.

Leveraging Segment Anything Model for Source-Free Domain Adaptation via Dual Feature Guided Auto-Prompting A-eval: A benchmark for cross-dataset and cross- modality evaluation of abdominal multi-organ segmentation,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:57:53.760433Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation a2721f5d-4325-47af-91ee-9e8e4e157208 · outbound

This paper cites Patch-based output space adversarial learning for joint optic disc and cup segmentation,.

Leveraging Segment Anything Model for Source-Free Domain Adaptation via Dual Feature Guided Auto-Prompting Patch-based output space adversarial learning for joint optic disc and cup segmentation,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:57:53.747042Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 2893e07f-97cc-485f-bb28-d7ad462b15f7 · outbound

This paper cites Do we really need to access the source data? source hypothesis transfer for unsupervised domain adaptation,.

Leveraging Segment Anything Model for Source-Free Domain Adaptation via Dual Feature Guided Auto-Prompting Do we really need to access the source data? source hypothesis transfer for unsupervised domain adaptation,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:57:53.733745Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 57071726-375e-4d23-91b4-0a02e3b56a21 · outbound

This paper cites Source-free domain adaptation via distribution estimation,.

Leveraging Segment Anything Model for Source-Free Domain Adaptation via Dual Feature Guided Auto-Prompting Source-free domain adaptation via distribution estimation,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:57:53.720264Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation ea8cf806-66d4-4384-8a16-8005a81806ec · outbound

This paper cites Uncertainty reduction for model adaptation in semantic segmentation,.

Leveraging Segment Anything Model for Source-Free Domain Adaptation via Dual Feature Guided Auto-Prompting Uncertainty reduction for model adaptation in semantic segmentation,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:57:53.706613Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 6835f6b7-1c98-47d2-818b-bea8c86547d3 · outbound

This paper cites Uncertainty-guided source-free domain adaptation,.

Leveraging Segment Anything Model for Source-Free Domain Adaptation via Dual Feature Guided Auto-Prompting Uncertainty-guided source-free domain adaptation,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:57:53.693979Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 382d6da1-a3d9-4bd8-ac60-55409b0ee9b5 · outbound

This paper cites Class relationship embedded learning for source-free unsupervised domain adaptation,.

Leveraging Segment Anything Model for Source-Free Domain Adaptation via Dual Feature Guided Auto-Prompting Class relationship embedded learning for source-free unsupervised domain adaptation,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:57:53.681343Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation f7460425-1712-4688-9df3-95cad92ad793 · outbound

This paper cites Source-free domain adaptive fundus image segmentation with denoised pseudo-labeling,.

Leveraging Segment Anything Model for Source-Free Domain Adaptation via Dual Feature Guided Auto-Prompting Source-free domain adaptive fundus image segmentation with denoised pseudo-labeling,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:57:53.668640Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 48d62991-6993-4978-b5a2-cbc33df458b4 · outbound

This paper cites Denoising for relaxing: Unsupervised domain adaptive fundus image segmentation without source data,.

Leveraging Segment Anything Model for Source-Free Domain Adaptation via Dual Feature Guided Auto-Prompting Denoising for relaxing: Unsupervised domain adaptive fundus image segmentation without source data,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:57:53.655824Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 6b4db738-18ba-4e98-b47e-1fbecee43f2f · outbound

This paper cites Upl-sfda: Uncertainty-aware pseudo label guided source-free domain adaptation for medical image segmentation,.

Leveraging Segment Anything Model for Source-Free Domain Adaptation via Dual Feature Guided Auto-Prompting Upl-sfda: Uncertainty-aware pseudo label guided source-free domain adaptation for medical image segmentation,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:57:53.642524Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation f95f356b-fd04-4da5-bcf3-a93489237a8f · outbound

This paper cites Source free domain adaptation for medical image segmentation with fourier style mining,.

Leveraging Segment Anything Model for Source-Free Domain Adaptation via Dual Feature Guided Auto-Prompting Source free domain adaptation for medical image segmentation with fourier style mining,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:57:53.629558Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 1f95e146-47e9-49e1-a56c-ff681b07615a · outbound

This paper cites Fvp: Fourier visual prompting for source-free unsupervised domain adaptation of medical image segmentation,.

Leveraging Segment Anything Model for Source-Free Domain Adaptation via Dual Feature Guided Auto-Prompting Fvp: Fourier visual prompting for source-free unsupervised domain adaptation of medical image segmentation,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:57:53.615632Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 07f7d5db-1e94-4053-8b60-c09462e771cf · outbound

This paper cites Source- free domain adaptation for image segmentation,.

Leveraging Segment Anything Model for Source-Free Domain Adaptation via Dual Feature Guided Auto-Prompting Source- free domain adaptation for image segmentation,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:57:53.602301Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation b37c336a-5f2a-4fd3-847a-da048a9a0a15 · outbound

This paper cites Source- free domain adaptation for medical image segmentation via prototype- anchored feature alignment and contrastive learning,.

Leveraging Segment Anything Model for Source-Free Domain Adaptation via Dual Feature Guided Auto-Prompting Source- free domain adaptation for medical image segmentation via prototype- anchored feature alignment and contrastive learning,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:57:53.588971Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 7fd02b93-cfea-442b-8981-523a02e809d4 · outbound

This paper cites Segment anything,.

Leveraging Segment Anything Model for Source-Free Domain Adaptation via Dual Feature Guided Auto-Prompting Segment anything,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:57:53.574052Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 73bd3276-f937-42be-8d6b-0bdc2e045990 · outbound

This paper cites Customized Segment Anything Model for Medical Image Segmentation.

Leveraging Segment Anything Model for Source-Free Domain Adaptation via Dual Feature Guided Auto-Prompting Customized Segment Anything Model for Medical Image Segmentation

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-15T21:57:53.087062Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 58ac12b0-3f29-4894-a299-e038c55e11c4 · outbound

This paper cites MA-SAM: Modality-agnostic SAM Adaptation for 3D Medical Image Segmentation.

Leveraging Segment Anything Model for Source-Free Domain Adaptation via Dual Feature Guided Auto-Prompting MA-SAM: Modality-agnostic SAM Adaptation for 3D Medical Image Segmentation

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-15T21:57:53.091251Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 60b038ec-f0c0-422f-96fa-cbaa02c9a91e · outbound

This paper cites Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation.

Leveraging Segment Anything Model for Source-Free Domain Adaptation via Dual Feature Guided Auto-Prompting Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-15T21:57:53.095356Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ad5e900f-1e40-431a-9a3f-3c2560ee5d66 · outbound

This paper cites Segment anything in medical images,.

Leveraging Segment Anything Model for Source-Free Domain Adaptation via Dual Feature Guided Auto-Prompting Segment anything in medical images,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:57:53.561140Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 9838ee6a-1e6e-4f68-bbfc-07d9083ca7a6 · outbound

This paper cites SAM-Med3D: Towards General-purpose Segmentation Models for Volumetric Medical Images.

Leveraging Segment Anything Model for Source-Free Domain Adaptation via Dual Feature Guided Auto-Prompting SAM-Med3D: Towards General-purpose Segmentation Models for Volumetric Medical Images

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-15T21:57:53.103312Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1a2964cd-5d63-48d3-b367-9c3ece910ecf · outbound

This paper cites Beyond adapting sam: Towards end-to-end ultrasound image segmentation via auto prompting,.

Leveraging Segment Anything Model for Source-Free Domain Adaptation via Dual Feature Guided Auto-Prompting Beyond adapting sam: Towards end-to-end ultrasound image segmentation via auto prompting,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:57:53.547971Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 5d6ade90-a861-47ff-af42-7bbf49fb457d · outbound

This paper cites MedSAM-U: Uncertainty-Guided Auto Multi-Prompt Adaptation for Reliable MedSAM.

Leveraging Segment Anything Model for Source-Free Domain Adaptation via Dual Feature Guided Auto-Prompting MedSAM-U: Uncertainty-Guided Auto Multi-Prompt Adaptation for Reliable MedSAM

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-15T21:57:53.111751Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 05121d0d-64f4-4f1b-9789-ddb6940957b2 · outbound

This paper cites Source-free domain adaptive fundus image segmentation with class-balanced mean teacher,.

Leveraging Segment Anything Model for Source-Free Domain Adaptation via Dual Feature Guided Auto-Prompting Source-free domain adaptive fundus image segmentation with class-balanced mean teacher,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:57:53.534738Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation ebc13e71-0c0e-4ee5-a6de-ce197156b13a · outbound

This paper cites Adapting off-the- shelf source segmenter for target medical image segmentation,.

Leveraging Segment Anything Model for Source-Free Domain Adaptation via Dual Feature Guided Auto-Prompting Adapting off-the- shelf source segmenter for target medical image segmentation,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:57:53.521567Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 48557c3f-3e07-4150-b8b2-28e3e16faf91 · outbound

This paper cites Learning transferable visual models from natural language supervision,.

Leveraging Segment Anything Model for Source-Free Domain Adaptation via Dual Feature Guided Auto-Prompting Learning transferable visual models from natural language supervision,

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-15T21:57:53.124228Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5f2f931e-3f21-4649-93df-c3d7d081950d · outbound

This paper cites Scaling up visual and vision-language representation learning with noisy text supervision,.

Leveraging Segment Anything Model for Source-Free Domain Adaptation via Dual Feature Guided Auto-Prompting Scaling up visual and vision-language representation learning with noisy text supervision,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:57:53.498936Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 989df311-68ef-4970-a31d-1b4459148c62 · outbound

This paper cites SegGPT: Segmenting Everything In Context.

Leveraging Segment Anything Model for Source-Free Domain Adaptation via Dual Feature Guided Auto-Prompting SegGPT: Segmenting Everything In Context

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-15T21:57:53.132171Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d8ae915c-071c-4a9d-8ab1-f68e5bd69b99 · outbound

This paper cites Rethinking the role of pre-trained networks in source-free domain adaptation,.

Leveraging Segment Anything Model for Source-Free Domain Adaptation via Dual Feature Guided Auto-Prompting Rethinking the role of pre-trained networks in source-free domain adaptation,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:57:53.484577Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation a9189b17-bb1c-465b-9d99-febaa226273c · outbound

This paper cites The unreasonable effectiveness of large language-vision models for source- free video domain adaptation,.

Leveraging Segment Anything Model for Source-Free Domain Adaptation via Dual Feature Guided Auto-Prompting The unreasonable effectiveness of large language-vision models for source- free video domain adaptation,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:57:53.471094Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 26ea47ce-ab2c-4825-9e57-7788537c0d41 · outbound

This paper cites Source-free domain adaptation with frozen multimodal foundation model,.

Leveraging Segment Anything Model for Source-Free Domain Adaptation via Dual Feature Guided Auto-Prompting Source-free domain adaptation with frozen multimodal foundation model,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:57:53.457199Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation c2527c72-7ab6-46ee-a1f4-3188fd6aeff8 · outbound

This paper cites Exploiting the intrinsic neighborhood structure for source-free domain adaptation,.

Leveraging Segment Anything Model for Source-Free Domain Adaptation via Dual Feature Guided Auto-Prompting Exploiting the intrinsic neighborhood structure for source-free domain adaptation,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:57:53.443684Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 59cfcf28-1e62-44ee-a6c4-33d7c850ebc6 · outbound

This paper cites BMD: A General Class-balanced Multicentric Dynamic Prototype Strategy for Source-free Domain Adaptation.

Leveraging Segment Anything Model for Source-Free Domain Adaptation via Dual Feature Guided Auto-Prompting BMD: A General Class-balanced Multicentric Dynamic Prototype Strategy for Source-free Domain Adaptation

Reference 34

Resolution: verified exact; local_arxiv, observed 2026-08-15T21:57:53.225904Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 6cd3d0c7-c6b0-4aa5-864e-62356df615d1 · outbound

This paper cites Exploiting chain rule and bayes’ theorem to compare probability distributions,.

Leveraging Segment Anything Model for Source-Free Domain Adaptation via Dual Feature Guided Auto-Prompting Exploiting chain rule and bayes’ theorem to compare probability distributions,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:57:53.429741Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 51ba4f69-2b57-4b11-8439-4c50bf3b219c · outbound

This paper cites Miccai multi-atlas labeling beyond the cranial vault–workshop and challenge,.

Leveraging Segment Anything Model for Source-Free Domain Adaptation via Dual Feature Guided Auto-Prompting Miccai multi-atlas labeling beyond the cranial vault–workshop and challenge,

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-15T21:57:53.160405Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1d51b96b-710d-471f-8d25-0b5390f3773d · outbound

This paper cites Chaos challenge- combined (ct-mr) healthy abdominal organ segmentation,.

Leveraging Segment Anything Model for Source-Free Domain Adaptation via Dual Feature Guided Auto-Prompting Chaos challenge- combined (ct-mr) healthy abdominal organ segmentation,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:57:53.407788Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 2bc6b748-51f5-4ec6-baf3-d2bcf7133106 · outbound

This paper cites Miccai calibration and uncertainty for multirater volume assessment in multiorgan segmentation challenge,.

Leveraging Segment Anything Model for Source-Free Domain Adaptation via Dual Feature Guided Auto-Prompting Miccai calibration and uncertainty for multirater volume assessment in multiorgan segmentation challenge,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:57:53.393737Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 057dea52-f265-47a0-a418-7a9e0f0be771 · outbound

This paper cites Domain adaptation meets zero-shot learning: an annotation-efficient approach to multi-modality medical image segmentation,.

Leveraging Segment Anything Model for Source-Free Domain Adaptation via Dual Feature Guided Auto-Prompting Domain adaptation meets zero-shot learning: an annotation-efficient approach to multi-modality medical image segmentation,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:57:53.378543Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 7ac9d6fb-077b-4955-9653-c471c32d4828 · outbound

This paper cites Unsupervised bidirectional cross-modality adaptation via deeply synergistic image and feature alignment for medical image segmentation,.

Leveraging Segment Anything Model for Source-Free Domain Adaptation via Dual Feature Guided Auto-Prompting Unsupervised bidirectional cross-modality adaptation via deeply synergistic image and feature alignment for medical image segmentation,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:57:53.363900Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation cb2ce17b-c685-45b9-b6f9-78d0792f1385 · outbound

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

Leveraging Segment Anything Model for Source-Free Domain Adaptation via Dual Feature Guided Auto-Prompting Nci- isbi 2013 challenge: Automated segmentation of prostate structures,

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:57:53.350232Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation bb941f48-a3cf-4b9c-935d-915510e895e5 · outbound

This paper cites Ms-net: Multi-site network for improving prostate segmentation with heterogeneous mri data,.

Leveraging Segment Anything Model for Source-Free Domain Adaptation via Dual Feature Guided Auto-Prompting Ms-net: Multi-site network for improving prostate segmentation with heterogeneous mri data,

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:57:53.336547Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 473725c1-6ab8-4fab-baca-0ba10d05dde6 · outbound

This paper cites Variability of manual segmen- tation of the prostate in axial t2-weighted mri: a multi-reader study,.

Leveraging Segment Anything Model for Source-Free Domain Adaptation via Dual Feature Guided Auto-Prompting Variability of manual segmen- tation of the prostate in axial t2-weighted mri: a multi-reader study,

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:57:53.322224Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers