SAM-DA: Decoder Adapter for Efficient Medical Domain Adaptation | Paper Citation Record

Observation 396e9c17-c14e-4275-bb71-8aa5dce4976e · outbound

This paper cites Neural Circuit Diagrams: Robust Diagrams for the Communication, Implementation, and Analysis of Deep Learning Architectures.

SAM-DA: Decoder Adapter for Efficient Medical Domain Adaptation Neural Circuit Diagrams: Robust Diagrams for the Communication, Implementation, and Analysis of Deep Learning Architectures

Reference 1

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Observation 503a1e19-ff23-4276-a9f2-6a2b9a5dc234 · outbound

This paper cites Retouch: The retinal oct fluid detection and segmenta- tion benchmark and challenge.

SAM-DA: Decoder Adapter for Efficient Medical Domain Adaptation Retouch: The retinal oct fluid detection and segmenta- tion benchmark and challenge

Reference 2

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Observation 5961fd94-b47d-499e-bef3-7396efa6c377 · outbound

This paper cites Pipa: Pixel-and patch-wise self-supervised learning for do- main adaptative semantic segmentation.

SAM-DA: Decoder Adapter for Efficient Medical Domain Adaptation Pipa: Pixel-and patch-wise self-supervised learning for do- main adaptative semantic segmentation

Reference 3

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Observation b9cf5448-ac78-46bb-8a18-2d98146ece79 · outbound

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

SAM-DA: Decoder Adapter for Efficient Medical Domain Adaptation Sam-adapter: Adapting segment anything in underperformed scenes

Reference 4

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Observation 5d3c0eab-2190-4e8a-b5f7-51e6271cd026 · outbound

This paper cites Vision Transformer Adapter for Dense Predictions.

SAM-DA: Decoder Adapter for Efficient Medical Domain Adaptation Vision Transformer Adapter for Dense Predictions

Reference 5

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Observation b83581f0-809c-402e-b65b-f6838c79ed9c · outbound

This paper cites Global contrast based salient region detection.

SAM-DA: Decoder Adapter for Efficient Medical Domain Adaptation Global contrast based salient region detection

Reference 6

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Observation 5b8c47d6-704f-4151-bc58-35a468ac832f · outbound

This paper cites An image is worth 16x16 words: Transformers for image recognition at scale.

SAM-DA: Decoder Adapter for Efficient Medical Domain Adaptation An image is worth 16x16 words: Transformers for image recognition at scale

Reference 7

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Observation 8c4dd309-1e1c-4b2f-a8b9-00716955cb6d · outbound

This paper cites Domain adaptation for med- ical image segmentation using transformation-invariant self- training.

SAM-DA: Decoder Adapter for Efficient Medical Domain Adaptation Domain adaptation for med- ical image segmentation using transformation-invariant self- training

Reference 8

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Observation ff486344-b399-48b0-a9d6-a3e39528ebf8 · outbound

This paper cites Towards a Unified View of Parameter-Efficient Transfer Learning.

SAM-DA: Decoder Adapter for Efficient Medical Domain Adaptation Towards a Unified View of Parameter-Efficient Transfer Learning

Reference 9

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Observation 9e345911-4de5-4f6b-81f3-efae254b18a5 · outbound

This paper cites Parameter-efficient transfer learning for nlp.

SAM-DA: Decoder Adapter for Efficient Medical Domain Adaptation Parameter-efficient transfer learning for nlp

Reference 10

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Observation c67a144d-c4de-43e1-8982-3bcc97b00337 · outbound

This paper cites Mic: Masked image consistency for context- enhanced domain adaptation.

SAM-DA: Decoder Adapter for Efficient Medical Domain Adaptation Mic: Masked image consistency for context- enhanced domain adaptation

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:53:12.438182Z

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Observation d981d261-e159-4ce7-8b42-4f872fb022b6 · outbound

This paper cites LoRA: Low- rank adaptation of large language models.

SAM-DA: Decoder Adapter for Efficient Medical Domain Adaptation LoRA: Low- rank adaptation of large language models

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:53:12.422525Z

Source-reported events for the cited work

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Observation 26dfbea4-f1d7-412b-a767-d16b42fe2ce3 · outbound

This paper cites Single Image Test-Time Adaptation for Segmentation.

SAM-DA: Decoder Adapter for Efficient Medical Domain Adaptation Single Image Test-Time Adaptation for Segmentation

Reference 13

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Observation 7fa1a7d9-200b-4b9d-b556-7e807564c87a · outbound

This paper cites Segment anything in high quality.

SAM-DA: Decoder Adapter for Efficient Medical Domain Adaptation Segment anything in high quality

Reference 14

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Observation 19264e93-e66b-482d-89a0-77d6fd7e8c43 · outbound

This paper cites Berg, Wan-Yen Lo, Piotr Doll ´ar, and Ross Girshick.

SAM-DA: Decoder Adapter for Efficient Medical Domain Adaptation Berg, Wan-Yen Lo, Piotr Doll ´ar, and Ross Girshick

Reference 15

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Observation dfb44bf9-948d-4040-afc2-bd7ae50ce2af · outbound

This paper cites Kuijf, Adria Casamitjana, D.

SAM-DA: Decoder Adapter for Efficient Medical Domain Adaptation Kuijf, Adria Casamitjana, D

Reference 16

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Observation f5a7ca02-3fd7-4b1d-b547-cd2bb66875fb · outbound

This paper cites Universal source-free domain adaptation.

SAM-DA: Decoder Adapter for Efficient Medical Domain Adaptation Universal source-free domain adaptation

Reference 17

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Observation c17a351d-0de1-459a-a4ac-2131eb1bdb6a · outbound

This paper cites The Power of Scale for Parameter-Efficient Prompt Tuning.

SAM-DA: Decoder Adapter for Efficient Medical Domain Adaptation The Power of Scale for Parameter-Efficient Prompt Tuning

Reference 18

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Observation 41ef8f31-856d-4390-bbe3-e1c29748d0e5 · outbound

This paper cites Fss-1000: A 1000-class dataset for few- shot segmentation.

SAM-DA: Decoder Adapter for Efficient Medical Domain Adaptation Fss-1000: A 1000-class dataset for few- shot segmentation

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:53:12.333271Z

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Observation b793bebe-9357-47c2-b0d4-aa783b80a08b · outbound

This paper cites Prefix-Tuning: Optimizing Continuous Prompts for Generation.

SAM-DA: Decoder Adapter for Efficient Medical Domain Adaptation Prefix-Tuning: Optimizing Continuous Prompts for Generation

Reference 20

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Observation 1846f407-9f8b-4aa9-bd31-87d0476f4dce · outbound

This paper cites Deep interactive thin object selection.

SAM-DA: Decoder Adapter for Efficient Medical Domain Adaptation Deep interactive thin object selection

Reference 21

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Source-reported events for the cited work

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Observation ca7ac7ed-630a-43c3-8353-c670790215c6 · outbound

This paper cites Focal loss for dense object detection.

SAM-DA: Decoder Adapter for Efficient Medical Domain Adaptation Focal loss for dense object detection

Reference 22

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Source-reported events for the cited work

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Observation 74bf1575-d75c-48f2-a573-68bacff32fd0 · outbound

This paper cites Exploring Versatile Generative Language Model Via Parameter-Efficient Transfer Learning.

SAM-DA: Decoder Adapter for Efficient Medical Domain Adaptation Exploring Versatile Generative Language Model Via Parameter-Efficient Transfer Learning

Reference 23

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Source-reported events for the cited work

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Observation 0316d570-c167-45dc-8504-d15ef41e2969 · outbound

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

SAM-DA: Decoder Adapter for Efficient Medical Domain Adaptation Shape-aware meta-learning for generalizing prostate mri segmentation to unseen domains

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:53:12.290901Z

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Observation 6d092273-ced0-4d74-a095-dab46ab5c19b · outbound

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

SAM-DA: Decoder Adapter for Efficient Medical Domain Adaptation Ms- net: Multi-site network for improving prostate segmentation with heterogeneous mri data

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:53:12.273749Z

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Observation 6dcda393-7fbe-409e-8941-e0e2d0d20833 · outbound

This paper cites Gpt understands, too.

SAM-DA: Decoder Adapter for Efficient Medical Domain Adaptation Gpt understands, too

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:53:12.252515Z

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Observation bc2a14fe-eaaf-43e4-b0d5-bf06d805a57c · outbound

This paper cites Decoupled Weight Decay Regularization.

SAM-DA: Decoder Adapter for Efficient Medical Domain Adaptation Decoupled Weight Decay Regularization

Reference 27

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Source-reported events for the cited work

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Observation bc65edb1-1b60-4408-9ec7-669d3f6b5168 · outbound

This paper cites Segment anything in medical images.

SAM-DA: Decoder Adapter for Efficient Medical Domain Adaptation Segment anything in medical images

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:53:12.231125Z

Source-reported events for the cited work

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Observation bef4c693-0646-442d-ba06-21e01850c2c8 · outbound

This paper cites Parameter-efficient Multi-task Fine-tuning for Transformers via Shared Hypernetworks.

SAM-DA: Decoder Adapter for Efficient Medical Domain Adaptation Parameter-efficient Multi-task Fine-tuning for Transformers via Shared Hypernetworks

Reference 29

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Observation 87856e01-ff8d-4fdc-9073-b8331544128d · outbound

This paper cites Milletari, N.

SAM-DA: Decoder Adapter for Efficient Medical Domain Adaptation Milletari, N

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:53:12.215904Z

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Observation 7358a2a6-2b4f-4271-a4fc-0cac761e566a · outbound

This paper cites DINOv2: Learning Robust Visual Features without Supervision.

SAM-DA: Decoder Adapter for Efficient Medical Domain Adaptation DINOv2: Learning Robust Visual Features without Supervision

Reference 31

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Observation 7f4ddd81-b3ab-43c7-8f7d-8c81655e0bf6 · outbound

This paper cites AdapterFusion: Non-Destructive Task Composition for Transfer Learning.

SAM-DA: Decoder Adapter for Efficient Medical Domain Adaptation AdapterFusion: Non-Destructive Task Composition for Transfer Learning

Reference 32

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Observation 160f892c-fde4-43f6-8db3-e4aed386f25a · outbound

This paper cites Highly accurate dichotomous image segmentation.

SAM-DA: Decoder Adapter for Efficient Medical Domain Adaptation Highly accurate dichotomous image segmentation

Reference 33

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Observation baff3052-f6db-45a6-bdd9-24ad4de9038b · outbound

This paper cites Transfusion: Understanding transfer learning for medical imaging.

SAM-DA: Decoder Adapter for Efficient Medical Domain Adaptation Transfusion: Understanding transfer learning for medical imaging

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:53:12.168260Z

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 94ec13b7-98cb-4358-9d39-563b2a6911df · outbound

This paper cites Hierar- chical image saliency detection on extended cssd.

SAM-DA: Decoder Adapter for Efficient Medical Domain Adaptation Hierar- chical image saliency detection on extended cssd

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:53:12.152353Z

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Observation 384793b4-f839-4dac-b538-d247ecf5c033 · outbound

This paper cites LLaMA: Open and Efficient Foundation Language Models.

SAM-DA: Decoder Adapter for Efficient Medical Domain Adaptation LLaMA: Open and Efficient Foundation Language Models

Reference 36

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Observation 93354d74-dc7e-40d9-b362-973c31b03bc6 · outbound

This paper cites Tent: Fully test-time adaptation by entropy minimization.

SAM-DA: Decoder Adapter for Efficient Medical Domain Adaptation Tent: Fully test-time adaptation by entropy minimization

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:53:12.136354Z

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Observation e5ab674d-950e-4a21-afda-95595f88ee9d · outbound

This paper cites Dynamically instance- guided adaptation: A backward-free approach for test-time domain adaptive semantic segmentation.

SAM-DA: Decoder Adapter for Efficient Medical Domain Adaptation Dynamically instance- guided adaptation: A backward-free approach for test-time domain adaptive semantic segmentation

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:53:12.118449Z

Source-reported events for the cited work

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Observation 2548a619-356c-458e-bfcf-16c8d7b13d05 · outbound

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

SAM-DA: Decoder Adapter for Efficient Medical Domain Adaptation Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation

Reference 39

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Source-reported events for the cited work

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Observation a8c4c5a0-fbdb-46cc-b7f0-8930ce43f381 · outbound

This paper cites Adap- tive adversarial network for source-free domain adaptation.

SAM-DA: Decoder Adapter for Efficient Medical Domain Adaptation Adap- tive adversarial network for source-free domain adaptation

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:53:12.102862Z

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Observation dcb98250-a49d-417b-89b8-98e962c12007 · outbound

This paper cites Dual modality prompt tuning for vision-language pre-trained model.

SAM-DA: Decoder Adapter for Efficient Medical Domain Adaptation Dual modality prompt tuning for vision-language pre-trained model

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-10T20:53:11.653823Z

Source-reported events for the cited work

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Observation de7f659e-1e4c-46af-b9c2-cbb3dd76e0ed · outbound

This paper cites Parameter-Efficient Fine-Tuning Methods for Pretrained Language Models: A Critical Review and Assessment.

SAM-DA: Decoder Adapter for Efficient Medical Domain Adaptation Parameter-Efficient Fine-Tuning Methods for Pretrained Language Models: A Critical Review and Assessment

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-10T20:53:11.658520Z

Source-reported events for the cited work

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Observation f14d42fe-fc42-4f1e-ab9e-69ba40d21f95 · outbound

This paper cites Saliency detection via graph-based man- ifold ranking.

SAM-DA: Decoder Adapter for Efficient Medical Domain Adaptation Saliency detection via graph-based man- ifold ranking

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:53:12.077075Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 34865ab6-542a-4983-9584-075946edc3ce · outbound

This paper cites Taskonomy: Disentangling task transfer learning.

SAM-DA: Decoder Adapter for Efficient Medical Domain Adaptation Taskonomy: Disentangling task transfer learning

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-10T20:53:11.668258Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4a02084b-5a04-4b56-9c2c-4737e833f9a5 · outbound

This paper cites Towards high-resolution salient object detec- tion.

SAM-DA: Decoder Adapter for Efficient Medical Domain Adaptation Towards high-resolution salient object detec- tion

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:53:12.051480Z

Source-reported events for the cited work

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Observation e6c0c3c9-4366-4248-b398-caa0f6c8c0ab · outbound

This paper cites A Comprehensive Survey on Segment Anything Model for Vision and Beyond.

SAM-DA: Decoder Adapter for Efficient Medical Domain Adaptation A Comprehensive Survey on Segment Anything Model for Vision and Beyond

Reference 46

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Source-reported events for the cited work

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Observation 1dface72-5179-49de-a4f0-220ee72db3aa · outbound

This paper cites Improving the Generalization of Segmentation Foundation Model under Distribution Shift via Weakly Supervised Adaptation.

SAM-DA: Decoder Adapter for Efficient Medical Domain Adaptation Improving the Generalization of Segmentation Foundation Model under Distribution Shift via Weakly Supervised Adaptation

Reference 47

Resolution: verified exact; local_arxiv, observed 2026-08-10T20:53:11.777592Z

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Observation 63d3a45f-bc98-4faa-99fc-131cff5d134d · outbound

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

SAM-DA: Decoder Adapter for Efficient Medical Domain Adaptation Customized Segment Anything Model for Medical Image Segmentation

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-10T20:53:11.686683Z

Source-reported events for the cited work

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Observation f2f779d4-e142-46ca-b8ac-137a92f8a862 · outbound

This paper cites LLaMA-Adapter: Efficient Fine-tuning of Language Models with Zero-init Attention.

SAM-DA: Decoder Adapter for Efficient Medical Domain Adaptation LLaMA-Adapter: Efficient Fine-tuning of Language Models with Zero-init Attention

Reference 49

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Source-reported events for the cited work

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Observation 444c21bf-20c6-48d5-811c-8eb90508d284 · outbound

This paper cites Learning to prompt for vision-language models.

SAM-DA: Decoder Adapter for Efficient Medical Domain Adaptation Learning to prompt for vision-language models

Reference 50

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Source-reported events for the cited work

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Observation 1ea2c45d-22dc-4fd9-a9db-c74b22f50a85 · outbound

This paper cites Segment everything every- where all at once.

SAM-DA: Decoder Adapter for Efficient Medical Domain Adaptation Segment everything every- where all at once

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:53:12.025919Z

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Observation e738e10f-ed24-4ea6-9bf8-bb0996f4a94c · outbound

This paper cites an unresolved cited work.

SAM-DA: Decoder Adapter for Efficient Medical Domain Adaptation Unresolved cited work

Reference 2016

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