Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation | Paper Citation Record

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SAM 2: Segment Anything in Images and Videos cites this paper.

SAM 2: Segment Anything in Images and Videos Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation

Reference 28

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Observation 24db65d7-8f5c-4089-af31-97490fbbf153 · inbound

TP-UNet: Temporal Prompt Guided UNet for Medical Image Segmentation cites this paper.

TP-UNet: Temporal Prompt Guided UNet for Medical Image Segmentation Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation

Reference 6

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Video-to-Task Learning via Motion-Guided Attention for Few-Shot Action Recognition cites this paper.

Video-to-Task Learning via Motion-Guided Attention for Few-Shot Action Recognition Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation

Reference 36

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Optimized Vessel Segmentation: A Structure-Agnostic Approach with Small Vessel Enhancement and Morphological Correction cites this paper.

Optimized Vessel Segmentation: A Structure-Agnostic Approach with Small Vessel Enhancement and Morphological Correction Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation

Reference 23

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SPA: Efficient User-Preference Alignment against Uncertainty in Medical Image Segmentation cites this paper.

SPA: Efficient User-Preference Alignment against Uncertainty in Medical Image Segmentation Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation

Reference 2023

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A SAM-guided and Match-based Semi-Supervised Segmentation Framework for Medical Imaging cites this paper.

A SAM-guided and Match-based Semi-Supervised Segmentation Framework for Medical Imaging Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation

Reference 10

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SAM-MPA: Applying SAM to Few-shot Medical Image Segmentation using Mask Propagation and Auto-prompting cites this paper.

SAM-MPA: Applying SAM to Few-shot Medical Image Segmentation using Mask Propagation and Auto-prompting Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation

Reference 24

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Observation 5c459460-938f-4c18-897d-c149c66c0e50 · inbound

vesselFM: A Foundation Model for Universal 3D Blood Vessel Segmentation cites this paper.

vesselFM: A Foundation Model for Universal 3D Blood Vessel Segmentation Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation

Reference 56

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PDZSeg: Adapting the Foundation Model for Dissection Zone Segmentation with Visual Prompts in Robot-assisted Endoscopic Submucosal Dissection cites this paper.

PDZSeg: Adapting the Foundation Model for Dissection Zone Segmentation with Visual Prompts in Robot-assisted Endoscopic Submucosal Dissection Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation

Reference 13

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Observation 066ba7b0-eb8c-4dc6-8b9d-8c6f27240a2a · inbound

SimCMF: A Simple Cross-modal Fine-tuning Strategy from Vision Foundation Models to Any Imaging Modality cites this paper.

SimCMF: A Simple Cross-modal Fine-tuning Strategy from Vision Foundation Models to Any Imaging Modality Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation

Reference 88

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Observation 6229ef8f-a0f9-42d1-8f5d-5303d1337028 · inbound

Adaptive Interactive Segmentation for Multimodal Medical Imaging via Selection Engine cites this paper.

Adaptive Interactive Segmentation for Multimodal Medical Imaging via Selection Engine Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation

Reference 16

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Observation a6ee574e-c9a7-4e77-b1ee-37adf9c79fb8 · inbound

Inspiring the Next Generation of Segment Anything Models: Comprehensively Evaluate SAM and SAM 2 with Diverse Prompts Towards Context-Dependent Concepts under Different Scenes cites this paper.

Inspiring the Next Generation of Segment Anything Models: Comprehensively Evaluate SAM and SAM 2 with Diverse Prompts Towards Context-Dependent Concepts under Different Scenes Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation

Reference 85

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Observation 60e5fd24-ffed-4581-b22d-f5d6c3979af0 · inbound

EchoONE: Segmenting Multiple echocardiography Planes in One Model cites this paper.

EchoONE: Segmenting Multiple echocardiography Planes in One Model Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation

Reference 39

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Customize Segment Anything Model for Multi-Modal Semantic Segmentation with Mixture of LoRA Experts cites this paper.

Customize Segment Anything Model for Multi-Modal Semantic Segmentation with Mixture of LoRA Experts Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation

Reference 5

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BATseg: Boundary-aware Multiclass Spinal Cord Tumor Segmentation on 3D MRI Scans cites this paper.

BATseg: Boundary-aware Multiclass Spinal Cord Tumor Segmentation on 3D MRI Scans Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation

Reference 35

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Observation df3962db-9a21-411c-a05a-27ad64e498f2 · inbound

Lightweight Method for Interactive 3D Medical Image Segmentation with Multi-Round Result Fusion cites this paper.

Lightweight Method for Interactive 3D Medical Image Segmentation with Multi-Round Result Fusion Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation

Reference 7

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SAM-Mamba: Mamba Guided SAM Architecture for Generalized Zero-Shot Polyp Segmentation cites this paper.

SAM-Mamba: Mamba Guided SAM Architecture for Generalized Zero-Shot Polyp Segmentation Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-11T17:48:56.140095Z

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Observation 84a2ed20-7f29-4850-8c26-fdf6b98dc6ae · inbound

SEG-SAM: Semantic-Guided SAM for Unified Medical Image Segmentation cites this paper.

SEG-SAM: Semantic-Guided SAM for Unified Medical Image Segmentation Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation

Reference 34

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Observation fd5cb0b9-aa6a-48ee-8f02-8e7a568eb65e · inbound

In-context learning for medical image segmentation cites this paper.

In-context learning for medical image segmentation Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation

Reference 13

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Observation 84bf10a6-d222-44e3-9307-910556f756fb · inbound

Learnable Prompting SAM-induced Knowledge Distillation for Semi-supervised Medical Image Segmentation cites this paper.

Learnable Prompting SAM-induced Knowledge Distillation for Semi-supervised Medical Image Segmentation Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation

Reference 19

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MultiverSeg: Scalable Interactive Segmentation of Biomedical Imaging Datasets with In-Context Guidance cites this paper.

MultiverSeg: Scalable Interactive Segmentation of Biomedical Imaging Datasets with In-Context Guidance Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation

Reference 133

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Observation 0cb31ff2-c329-40e9-a660-084d1a83b222 · inbound

When SAM2 Meets Video Shadow and Mirror Detection cites this paper.

When SAM2 Meets Video Shadow and Mirror Detection Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation

Reference 14

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Observation 96f3d222-8739-4977-898b-cf9a742e952d · inbound

Char-SAM: Turning Segment Anything Model into Scene Text Segmentation Annotator with Character-level Visual Prompts cites this paper.

Char-SAM: Turning Segment Anything Model into Scene Text Segmentation Annotator with Character-level Visual Prompts Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation

Reference 26

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Observation 40d8410c-8c28-4fb1-b6c1-cb377c2d675c · inbound

Advanced Lung Nodule Segmentation and Classification for Early Detection of Lung Cancer using SAM and Transfer Learning cites this paper.

Advanced Lung Nodule Segmentation and Classification for Early Detection of Lung Cancer using SAM and Transfer Learning Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation

Reference 14

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Evidential Calibrated Uncertainty-Guided Interactive Segmentation paradigm for Ultrasound Images cites this paper.

Evidential Calibrated Uncertainty-Guided Interactive Segmentation paradigm for Ultrasound Images Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation

Reference 21

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SAM-DA: Decoder Adapter for Efficient Medical Domain Adaptation cites this paper.

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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Comparative Analysis of Pre-trained Deep Learning Models and DINOv2 for Cushing's Syndrome Diagnosis in Facial Analysis cites this paper.

Comparative Analysis of Pre-trained Deep Learning Models and DINOv2 for Cushing's Syndrome Diagnosis in Facial Analysis Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation

Reference 25

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Gland Segmentation Using SAM With Cancer Grade as a Prompt cites this paper.

Gland Segmentation Using SAM With Cancer Grade as a Prompt Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation

Reference 15

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Tuning Vision Foundation Model via Test-Time Prompt-Guided Training for VFSS Segmentations cites this paper.

Tuning Vision Foundation Model via Test-Time Prompt-Guided Training for VFSS Segmentations Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation

Reference 9

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Foundational Models for 3D Point Clouds: A Survey and Outlook cites this paper.

Foundational Models for 3D Point Clouds: A Survey and Outlook Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation

Reference 93

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Self-Prompt SAM: Medical Image Segmentation via Automatic Prompt SAM Adaptation cites this paper.

Self-Prompt SAM: Medical Image Segmentation via Automatic Prompt SAM Adaptation Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation

Reference 29

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SAM-guided Pseudo Label Enhancement for Multi-modal 3D Semantic Segmentation cites this paper.

SAM-guided Pseudo Label Enhancement for Multi-modal 3D Semantic Segmentation Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation

Reference 46

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IMDPrompter: Adapting SAM to Image Manipulation Detection by Cross-View Automated Prompt Learning cites this paper.

IMDPrompter: Adapting SAM to Image Manipulation Detection by Cross-View Automated Prompt Learning Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation

Reference 29

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Observation c5fd905f-8e00-4c57-be4c-42c5cb55db63 · inbound

Proxy Prompt: Endowing SAM and SAM 2 with Auto-Interactive-Prompt for Medical Segmentation cites this paper.

Proxy Prompt: Endowing SAM and SAM 2 with Auto-Interactive-Prompt for Medical Segmentation Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation

Reference 37

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Towards Fine-grained Interactive Segmentation in Images and Videos cites this paper.

Towards Fine-grained Interactive Segmentation in Images and Videos Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation

Reference 36

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COMMA: Coordinate-aware Modulated Mamba Network for 3D Dispersed Vessel Segmentation cites this paper.

COMMA: Coordinate-aware Modulated Mamba Network for 3D Dispersed Vessel Segmentation Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation

Reference 49

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Putting the Segment Anything Model to the Test with 3D Knee MRI - A Comparison with State-of-the-Art Performance cites this paper.

Putting the Segment Anything Model to the Test with 3D Knee MRI - A Comparison with State-of-the-Art Performance Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation

Reference 40

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IXGS-Intraoperative 3D Reconstruction from Sparse, Arbitrarily Posed Real X-rays cites this paper.

IXGS-Intraoperative 3D Reconstruction from Sparse, Arbitrarily Posed Real X-rays Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation

Reference 44

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Federated Client-tailored Adapter for Medical Image Segmentation cites this paper.

Federated Client-tailored Adapter for Medical Image Segmentation Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation

Reference 8

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SAM-Guided Robust Representation Learning for One-Shot 3D Medical Image Segmentation cites this paper.

SAM-Guided Robust Representation Learning for One-Shot 3D Medical Image Segmentation Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation

Reference 18

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Mix-QSAM: Mixed-Precision Quantization of the Segment Anything Model cites this paper.

Mix-QSAM: Mixed-Precision Quantization of the Segment Anything Model Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation

Reference 50

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Causal Prompt Calibration Guided Segment Anything Model for Open-Vocabulary Multi-Entity Segmentation cites this paper.

Causal Prompt Calibration Guided Segment Anything Model for Open-Vocabulary Multi-Entity Segmentation Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation

Reference 60

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Semantic-Guided Diffusion Model for Single-Step Image Super-Resolution cites this paper.

Semantic-Guided Diffusion Model for Single-Step Image Super-Resolution Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation

Reference 32

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

Leveraging Segment Anything Model for Source-Free Domain Adaptation via Dual Feature Guided Auto-Prompting cites this paper.

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

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Parameter-Efficient Fine-Tuning of Vision Foundation Model for Forest Floor Segmentation from UAV Imagery cites this paper.

Parameter-Efficient Fine-Tuning of Vision Foundation Model for Forest Floor Segmentation from UAV Imagery Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation

Reference 10

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Promoting SAM for Camouflaged Object Detection via Selective Key Point-based Guidance cites this paper.

Promoting SAM for Camouflaged Object Detection via Selective Key Point-based Guidance Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation

Reference 36

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Observation e34f4bbf-2600-45c9-aac8-4d7295425360 · inbound

Recent Advances in Medical Imaging Segmentation: A Survey cites this paper.

Recent Advances in Medical Imaging Segmentation: A Survey Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation

Reference 92

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TAGS: 3D Tumor-Adaptive Guidance for SAM cites this paper.

TAGS: 3D Tumor-Adaptive Guidance for SAM Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation

Reference 56

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Observation 8fa47d21-827d-415a-8d7b-019097e79d86 · inbound

SAM-I2V: Upgrading SAM to Support Promptable Video Segmentation with Less than 0.2% Training Cost cites this paper.

SAM-I2V: Upgrading SAM to Support Promptable Video Segmentation with Less than 0.2% Training Cost Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation

Reference 43

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Observation 9cb773e5-94e0-48c3-ab0e-922210878095 · inbound

SSS: Semi-Supervised SAM-2 with Efficient Prompting for Medical Imaging Segmentation cites this paper.

SSS: Semi-Supervised SAM-2 with Efficient Prompting for Medical Imaging Segmentation Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-07T05:03:36.359237Z

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Observation 6cff38be-7265-47a7-b656-36aa375fb443 · inbound

SRPL-SFDA: SAM-Guided Reliable Pseudo-Labels for Source-Free Domain Adaptation in Medical Image Segmentation cites this paper.

SRPL-SFDA: SAM-Guided Reliable Pseudo-Labels for Source-Free Domain Adaptation in Medical Image Segmentation Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-07T04:56:11.853828Z

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Observation f4b6dac5-140d-4110-93d0-9826b0496878 · inbound

Brain Imaging Foundation Models, Are We There Yet? A Systematic Review of Foundation Models for Brain Imaging and Biomedical Research cites this paper.

Brain Imaging Foundation Models, Are We There Yet? A Systematic Review of Foundation Models for Brain Imaging and Biomedical Research Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation

Reference 157

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FocalClick-XL: Towards Unified and High-quality Interactive Segmentation cites this paper.

FocalClick-XL: Towards Unified and High-quality Interactive Segmentation Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation

Reference 38

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Observation c8bfa71c-96e0-4c99-88ca-1222d792136d · inbound

BCRNet: Enhancing Landmark Detection in Laparoscopic Liver Surgery via Bezier Curve Refinement cites this paper.

BCRNet: Enhancing Landmark Detection in Laparoscopic Liver Surgery via Bezier Curve Refinement Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-15T19:40:27.570664Z

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Observation 3e08f683-eb1a-4e79-89ba-7915a4b7d6a7 · inbound

Segment Anything for Satellite Imagery: A Strong Baseline and a Regional Dataset for Automatic Field Delineation cites this paper.

Segment Anything for Satellite Imagery: A Strong Baseline and a Regional Dataset for Automatic Field Delineation Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-06T23:48:24.401174Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 035cc532-524a-455b-857a-1b2b5b299513 · inbound

SAM2-SGP: Enhancing SAM2 for Medical Image Segmentation via Support-Set Guided Prompting cites this paper.

SAM2-SGP: Enhancing SAM2 for Medical Image Segmentation via Support-Set Guided Prompting Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-15T18:32:46.771154Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bcc2d600-a684-44c8-ac7f-6921269badb6 · inbound

Evolutionary computing-based image segmentation method to detect defects and features in Additive Friction Stir Deposition Process cites this paper.

Evolutionary computing-based image segmentation method to detect defects and features in Additive Friction Stir Deposition Process Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-06T23:10:16.251683Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5316cdec-2572-4d8f-b73d-9e6de7ce5019 · inbound

LoD-Loc v2: Aerial Visual Localization over Low Level-of-Detail City Models using Explicit Silhouette Alignment cites this paper.

LoD-Loc v2: Aerial Visual Localization over Low Level-of-Detail City Models using Explicit Silhouette Alignment Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation

Reference 85

Resolution: unresolved; no resolver link, observed 2026-08-06T21:16:48.265669Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation da19b629-5942-4572-b2df-aa2e5df9b4d2 · inbound

PromptForSegCXR: Prompt-Driven Multi-Organ and Multi-Disease Segmentation in Chest X-rays using a Multi-stage Fusion Mechanism cites this paper.

PromptForSegCXR: Prompt-Driven Multi-Organ and Multi-Disease Segmentation in Chest X-rays using a Multi-stage Fusion Mechanism Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-06T21:15:08.542922Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7003fdcf-c75c-436d-84d4-21016e5f4134 · inbound

Mamba Guided Boundary Prior Matters: A New Perspective for Generalized Polyp Segmentation cites this paper.

Mamba Guided Boundary Prior Matters: A New Perspective for Generalized Polyp Segmentation Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-06T20:55:07.752330Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cd4bfa4b-3cc3-4f63-b25a-dc6df4fad9d6 · inbound

Memory-Augmented SAM2 for Training-Free Surgical Video Segmentation cites this paper.

Memory-Augmented SAM2 for Training-Free Surgical Video Segmentation Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-06T17:58:46.645405Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5e2cbf5a-6a89-45f6-acf3-00868566ffe7 · inbound

Region-aware Depth Scale Adaptation with Sparse Measurements cites this paper.

Region-aware Depth Scale Adaptation with Sparse Measurements Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-06T15:53:59.233259Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 088652fa-86a7-492f-874b-7e17c73f54f0 · inbound

M-Net: MRI Brain Tumor Sequential Segmentation Network via Mesh-Cast cites this paper.

M-Net: MRI Brain Tumor Sequential Segmentation Network via Mesh-Cast Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-15T17:44:45.702801Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b58253a0-7441-4875-9dcd-ded5ed17c999 · inbound

Register Anything: Estimating "Corresponding Prompts" for Segment Anything Model cites this paper.

Register Anything: Estimating "Corresponding Prompts" for Segment Anything Model Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-06T05:30:10.841869Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f4e93cea-3af0-4a94-af03-348149ee46b8 · inbound

ParticleSAM: Small Particle Segmentation for Material Quality Monitoring in Recycling Processes cites this paper.

ParticleSAM: Small Particle Segmentation for Material Quality Monitoring in Recycling Processes Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-06T04:28:30.949233Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bf8ab717-4015-494f-947c-8c314b33e629 · inbound

MetaScope: Optics-Driven Neural Network for Ultra-Micro Metalens Endoscopy cites this paper.

MetaScope: Optics-Driven Neural Network for Ultra-Micro Metalens Endoscopy Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation

Reference 98

Resolution: unresolved; no resolver link, observed 2026-08-06T04:24:28.200681Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 494be88c-cfba-4e78-8b27-54990cf733be · inbound

SPGrasp: Spatiotemporal Prompt-driven Grasp Synthesis in Dynamic Scenes cites this paper.

SPGrasp: Spatiotemporal Prompt-driven Grasp Synthesis in Dynamic Scenes Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-15T16:47:20.145802Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e11ff524-d44d-4b46-b372-17654bd44545 · inbound

Multimodal SAM-adapter for Semantic Segmentation cites this paper.

Multimodal SAM-adapter for Semantic Segmentation Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-04T17:57:10.279609Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 98c77fc2-deef-4406-87df-c7a7cec3f22a · inbound

SegSLR: Promptable Video Segmentation for Isolated Sign Language Recognition cites this paper.

SegSLR: Promptable Video Segmentation for Isolated Sign Language Recognition Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-04T17:38:53.185249Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ddf2c4df-20da-49b8-b704-838914e8a694 · inbound

Towards Any-Quality Image Segmentation via Generative and Adaptive Latent Space Enhancement cites this paper.

Towards Any-Quality Image Segmentation via Generative and Adaptive Latent Space Enhancement Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation

Reference 80

Resolution: metadata mismatch; arxiv_id, observed 2026-05-16T18:13:13.281011Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 80ee9de7-bae0-4719-9aa5-2984ff2ffbfa · inbound

Learning to Synergize Semantic and Geometric Priors for Limited-Data Wheat Disease Segmentation cites this paper.

Learning to Synergize Semantic and Geometric Priors for Limited-Data Wheat Disease Segmentation Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation

Reference 28

Resolution: verified exact; arxiv_id, observed 2026-05-10T23:05:51.515675Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 4de3b2bb-2415-4552-abe4-4b7684a8dc76 · inbound

PR-MaGIC: Prompt Refinement Via Mask Decoder Gradient Flow For In-Context Segmentation cites this paper.

PR-MaGIC: Prompt Refinement Via Mask Decoder Gradient Flow For In-Context Segmentation Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation

Reference 28

Resolution: verified exact; arxiv_id, observed 2026-05-11T10:46:05.624882Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 4973f6ea-3ef2-490d-a79b-d281ddd82f0b · inbound

Align then Refine: Text-Guided 3D Prostate Lesion Segmentation cites this paper.

Align then Refine: Text-Guided 3D Prostate Lesion Segmentation Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation

Reference 9

Resolution: verified exact; arxiv_id, observed 2026-05-10T12:05:23.473716Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation c25849c9-984d-4a02-b663-83ee6c548294 · inbound

SAMamba3D: adapting Segment Anything for generalizable 3D segmentation of multiphase pore-scale images cites this paper.

SAMamba3D: adapting Segment Anything for generalizable 3D segmentation of multiphase pore-scale images Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation

Reference 20

Resolution: verified exact; arxiv_id, observed 2026-05-11T15:21:10.344103Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 1791d043-2dae-4005-a288-a6b7e7cb597f · inbound

Deep Reprogramming Distillation for Medical Foundation Models cites this paper.

Deep Reprogramming Distillation for Medical Foundation Models Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation

Reference 38

Resolution: verified exact; arxiv_id, observed 2026-05-09T06:25:38.776947Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation e9200b79-5c57-4814-ab6f-1ddfe4473745 · inbound

Any2Any 3D Diffusion Models with Knowledge Transfer: A Radiotherapy Planning Study cites this paper.

Any2Any 3D Diffusion Models with Knowledge Transfer: A Radiotherapy Planning Study Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation

Reference 66

Resolution: verified exact; arxiv_id, observed 2026-05-12T05:51:25.509104Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation cbad2151-86a3-4ab3-91d0-d3329bedd87f · inbound

Anatomy-Anchored Self-Supervision: Distilling Vision Foundation Models for Invariant Ultrasound Representation cites this paper.

Anatomy-Anchored Self-Supervision: Distilling Vision Foundation Models for Invariant Ultrasound Representation Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation

Reference 29

Resolution: metadata mismatch; arxiv_id, observed 2026-06-29T23:14:01.694934Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation b632804d-d66c-4507-af6c-f68732aacde2 · inbound

DeCoDrift: Stabilizing Decoder Coupling in Closed-Loop Foundation Segmentation cites this paper.

DeCoDrift: Stabilizing Decoder Coupling in Closed-Loop Foundation Segmentation Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation

Reference 8

Resolution: metadata mismatch; arxiv_id, observed 2026-06-29T23:14:01.837598Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 689a8632-3259-4bfb-951c-a450b6a25280 · inbound

Parameter-Efficient Fine-Tuning of Large Pretrained Models for Instance Segmentation Tasks cites this paper.

Parameter-Efficient Fine-Tuning of Large Pretrained Models for Instance Segmentation Tasks Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation

Reference 18

Resolution: verified exact; arxiv_id, observed 2026-07-01T22:36:17.211138Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 7df9dbdd-ca73-4091-becb-063002827486 · inbound

Zero-Shot Learning in Industrial Scenarios: New Large-Scale Benchmark, Challenges and Baseline cites this paper.

Zero-Shot Learning in Industrial Scenarios: New Large-Scale Benchmark, Challenges and Baseline Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation

Reference 87

Resolution: metadata mismatch; arxiv_id, observed 2026-07-02T20:57:23.227572Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation de097f89-60b4-41d8-887f-86a842eadeb4 · inbound

Parameter-Efficient Adaptation of SAM 3 for Automated ITV Generation from 4DCT Images cites this paper.

Parameter-Efficient Adaptation of SAM 3 for Automated ITV Generation from 4DCT Images Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation

Reference 22

Resolution: verified exact; arxiv_id, observed 2026-07-03T17:08:43.559845Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers