202312 |
Z. Zhao et al. |
One Model to Rule them All: Towards Universal Segmentation for Medical Images with Text Prompts (paper) |
Code |
202312 |
W. Yue et al. |
Part to Whole: Collaborative Prompting for Surgical Instrument Segmentation (paper) |
Code |
202312 |
ZM. Colbert et al. |
Repurposing Traditional U-Net Predictions for Sparse SAM Prompting in Medical Image Segmentation (paper) |
None |
202312 |
W. Xie et al. |
SAM Fewshot Finetuning for Anatomical Segmentation in Medical Images (paper) |
None |
202312 |
JG. Almeida et al. |
Testing the Segment Anything Model on radiology data (paper) |
None |
202312 |
M. Barakat et al. |
Towards SAMBA: Segment Anything Model for Brain Tumor Segmentation in Sub-Sharan African Populations (paper) |
None |
202312 |
Y. Zhang et al. |
SQA-SAM: Segmentation Quality Assessment for Medical Images Utilizing the Segment Anything Model (paper) |
Code |
202312 |
S. Chen et al. |
ASLseg: Adapting SAM in the Loop for Semi-supervised Liver Tumor Segmentation (paper) |
None |
202312 |
HE. Wong et al. |
ScribblePrompt: Fast and Flexible Interactive Segmentation for Any Medical Image (paper) |
Code |
202312 |
Y. Zhang et al. |
SemiSAM: Exploring SAM for Enhancing Semi-Supervised Medical Image Segmentation with Extremely Limited Annotations (paper) |
None |
202312 |
Y. Zhao et al. |
Segment Anything Model-guided Collaborative Learning Network for Scribble-supervised Polyp Segmentation (paper) |
None |
202311 |
N. Li et al. |
Segment Anything Model for Semi-Supervised Medical Image Segmentation via Selecting Reliable Pseudo-Labels (paper) |
None |
202311 |
X. Wei et al. |
I-MedSAM: Implicit Medical Image Segmentation with Segment Anything (paper) |
None |
202311 |
Z. Shui et al. |
Unleashing the Power of Prompt-driven Nucleus Instance Segmentation (paper) |
Code |
202311 |
M. Li and G. Yang et al. |
Where to Begin? From Random to Foundation Model Instructed Initialization in Federated Learning for Medical Image Segmentation (paper) |
None |
202311 |
AK. Tyagi et al. |
Guided Prompting in SAM for Weakly Supervised Cell Segmentation in Histopathological Images (paper) |
Code |
202311 |
Y. Du et al. |
SegVol: Universal and Interactive Volumetric Medical Image Segmentation (paper) |
Code |
202311 |
DM. Nguyen et al. |
On the Out of Distribution Robustness of Foundation Models in Medical Image Segmentation (paper) |
None |
202311 |
U. Israel et al. |
A Foundation Model for Cell Segmentation (paper) |
Code |
202311 |
Q. Quan et al. |
Slide-SAM: Medical SAM Meets Sliding Window (paper) |
None |
202311 |
Y. Zhang et al. |
Segment Anything Model with Uncertainty Rectification for Auto-Prompting Medical Image Segmentation (paper) |
Code |
202311 |
Y. Wang et al. |
SAMIHS: Adaptation of Segment Anything Model for Intracranial Hemorrhage Segmentation (paper) |
Code |
202311 |
H. Jiang et al. |
GlanceSeg: Real-time microangioma lesion segmentation with gaze map-guided foundation model for early detection of diabetic retinopathy (paper) |
None |
202311 |
Y. Xu et al. |
EviPrompt: A Training-Free Evidential Prompt Generation Method for Segment Anything Model in Medical Images (paper) |
None |
202311 |
DL. Ferreira and R. Arnaout |
Are foundation models efficient for medical image segmentation? (paper) |
Code |
202310 |
H. Li et al. |
Promise:Prompt-driven 3D Medical Image Segmentation Using Pretrained Image Foundation Models (paper) |
Code |
202310 |
D. Anand et al. |
One-shot Localization and Segmentation of Medical Images with Foundation Models (paper) |
None |
202310 |
H. Wang et al. |
SAM-Med3D (paper) |
Code |
202310 |
SK. Kim et al. |
Evaluation and improvement of Segment Anything Model for interactive histopathology image segmentation (paper) |
Code |
202310 |
X. Chen et al. |
SAM-OCTA: Prompting Segment-Anything for OCTA Image Segmentation (paper) |
Code |
202310 |
M. Peivandi et al. |
Empirical Evaluation of the Segment Anything Model (SAM) for Brain Tumor Segmentation (paper) |
None |
202310 |
H. Ravishankar et al. |
SonoSAM - Segment Anything on Ultrasound Images (paper) |
None |
202310 |
A. Ranem et al. |
Exploring SAM Ablations for Enhancing Medical Segmentation in Radiology and Pathology (paper) |
None |
202310 |
S. Pandey et al. |
Comprehensive Multimodal Segmentation in Medical Imaging: Combining YOLOv8 with SAM and HQ-SAM Models (paper) |
None |
202309 |
Y. Li et al. |
nnSAM: Plug-and-play Segment Anything Model Improves nnUNet Performance (paper) |
Code |
202309 |
Y. Zhao et al. |
MFS Enhanced SAM: Achieving Superior Performance in Bimodal Few-shot Segmentation (paper) |
Code |
202309 |
C. Wang et al. |
SAM-OCTA: A Fine-Tuning Strategy for Applying Foundation Model to OCTA Image Segmentation Tasks (paper) |
Code |
202309 |
Y. Zhang et al. |
3D-U-SAM Network For Few-shot Tooth Segmentation in CBCT Images (paper) |
None |
202309 |
CJ. Chao et al. |
Comparative Eminence: Foundation versus Domain-Specific Model for Cardiac Ultrasound Segmentation (paper) |
None |
202309 |
H. Ning et al. |
An Accurate and Efficient Neural Network for OCTA Vessel Segmentation and a New Dataset (paper) |
Code |
202309 |
C. Chen et al. |
MA-SAM: Modality-agnostic SAM Adaptation for 3D Medical Image Segmentation (paper) |
Code |
202309 |
P. Zhang and Y. Wang |
Segment Anything Model for Brain Tumor Segmentation (paper) |
None |
202309 |
B. Fazekas et al. |
Adapting Segment Anything Model (SAM) for Retinal OCT (paper) |
None |
202309 |
X. Lin et al. |
SAMUS: Adapting Segment Anything Model for Clinically-Friendly and Generalizable Ultrasound Image Segmentation (paper) |
Code |
202309 |
X. Xing et al. |
SegmentAnything helps microscopy images based automatic and quantitative organoid detection and analysis (paper) |
Code |
202309 |
NT. Bui et al. |
SAM3D: Segment Anything Model in Volumetric Medical Images (paper) |
Code |
202308 |
Y. Zhang et al. |
Self-Sampling Meta SAM: Enhancing Few-shot Medical Image Segmentation with Meta-Learning (paper) |
None |
202308 |
J. Cheng et al. |
SAM-Med2D (paper) |
Code |
202308 |
C. Li et al. |
Auto-Prompting SAM for Mobile Friendly 3D Medical Image Segmentation (paper) |
None |
202308 |
W. Feng et al. |
Cheap Lunch for Medical Image Segmentation by Fine-tuning SAM on Few Exemplars (paper) |
None |
202308 |
Y. Zhang et al. |
SamDSK: Combining Segment Anything Model with Domain-Specific Knowledge for Semi-Supervised Learning in Medical Image Segmentation (paper) |
None |
202308 |
A. Lou et al. |
SAMSNeRF: Segment Anything Model (SAM) Guides Dynamic Surgical Scene Reconstruction by Neural Radiance Field (NeRF) (paper) |
Code |
202308 |
A. Archit et al. |
Segment Anything for Microscopy (paper) |
Code |
202308 |
X. Yao et al. |
False Negative/Positive Control for SAM on Noisy Medical Images (paper) |
Code |
202308 |
B. Fazekas et al. |
SAMedOCT: Adapting Segment Anything Model (SAM) for Retinal OCT (paper) |
None |
202308 |
W. Yue et al. |
SurgicalSAM: Efficient Class Promptable Surgical Instrument Segmentation (paper) |
Code |
202308 |
H. Zhang et al. |
CARE: A Large Scale CT Image Dataset and Clinical Applicable Benchmark Model for Rectal Cancer Segmentation (paper) |
Code |
202308 |
Q. Wu et al. |
Self-Prompting Large Vision Models for Few-Shot Medical Image Segmentation (paper) |
Code |
202308 |
A. Wang et al. |
SAM Meets Robotic Surgery: An Empirical Study on Generalization, Robustness and Adaptation (paper) |
None |
202308 |
D. Shin et al. |
CEmb-SAM: Segment Anything Model with Condition Embedding for Joint Learning from Heterogeneous Datasets (paper) |
None |
202308 |
R. Biswas |
Polyp-SAM++: Can A Text Guided SAM Perform Better for Polyp Segmentation? (paper) |
Code |
202308 |
S. Cao et al. |
TongueSAM: An Universal Tongue Segmentation Model Based on SAM with Zero-Shot (paper) |
Code |
202308 |
X. Li et al. |
Leverage Weakly Annotation to Pixel-wise Annotation via Zero-shot Segment Anything Model for Molecular-empowered Learning (paper) |
None |
202308 |
JN. Paranjape et al. |
AdaptiveSAM: Towards Efficient Tuning of SAM for Surgical Scene Segmentation (paper) |
Code |
202308 |
Z. Huang et al. |
Push the Boundary of SAM: A Pseudo-label Correction Framework for Medical Segmentation (paper) |
None |
202307 |
J. Zhang et al. |
SAM-Path: A Segment Anything Model for Semantic Segmentation in Digital Pathology (paper) |
None |
202307 |
MS. Hossain et al. |
Robust HER2 Grading of Breast Cancer Patients using Zero-shot Segment Anything Model (SAM) (paper) |
None |
202307 |
C. Wang et al. |
SAM^Med^ : A medical image annotation framework based on large vision model (paper) |
None |
202307 |
G. Deng et al. |
SAM-U: Multi-box prompts triggered uncertainty estimation for reliable SAM in medical image (paper) |
None |
202307 |
H. Kim et al. |
Empirical Analysis of a Segmentation Foundation Model in Prostate Imaging (paper) |
None |
202307 |
X. Shi et al. |
Cross-modality Attention Adapter: A Glioma Segmentation Fine-tuning Method for SAM Using Multimodal Brain MR Images (paper) |
None |
202307 |
C. Cui et al. |
All-in-SAM: from Weak Annotation to Pixel-wise Nuclei Segmentation with Prompt-based Finetuning (paper) |
None |
202306 |
E. Kellener et al. |
Utilizing Segment Anything Model for Assessing Localization of Grad-CAM in Medical Imaging (paper) |
None |
202306 |
F. Hörst et al. |
CellViT: Vision Transformers for Precise Cell Segmentation and Classification (paper) |
Code |
202306 |
W. Lei et al. |
MedLSAM: Localize and Segment Anything Model for 3D Medical Images (paper) |
Code |
202306 |
X. Hu et al. |
How to Efficiently Adapt Large Segmentation Model (SAM) to Medical Images (paper) |
Code |
202306 |
S. Gong et al. |
3DSAM-adapter: Holistic Adaptation of SAM from 2D to 3D for Promptable Medical Image Segmentation (paper) |
Code |
202306 |
DMH. Nguyen et al. |
LVM-Med: Learning Large-Scale Self-Supervised Vision Models for Medical Imaging via Second-order Graph Matching (paper) |
Code |
202306 |
S. Chai et al. |
Ladder Fine-tuning approach for SAM integrating complementary network (paper) |
Code |
202306 |
L. Zhang et al. |
Segment Anything Model (SAM) for Radiation Oncology (paper) |
None |
202306 |
G. Ning et al. |
The potential of 'Segment Anything' (SAM) for universal intelligent ultrasound image guidance (paper) |
None |
202306 |
C. Shen et al. |
Temporally-Extended Prompts Optimization for SAM in Interactive Medical Image Segmentation (paper) |
None |
202306 |
T. Shaharabany et al. |
AutoSAM: Adapting SAM to Medical Images by Overloading the Prompt Encoder (paper) |
None |
202306 |
Y. Gao et al. |
DeSAM: Decoupling Segment Anything Model for Generalizable Medical Image Segmentation (paper) |
Code |
202305 |
D. Lee et al. |
IAMSAM : Image-based Analysis of Molecular signatures using the Segment-Anything Model (paper) |
Code |
202305 |
M. Hu et al. |
BreastSAM: A Study of Segment Anything Model for Breast Tumor Detection in Ultrasound Images (paper) |
None |
202305 |
J. Wu |
PromptUNet: Toward Interactive Medical Image Segmentation (paper) |
Code |
202305 |
Y. Li et al. |
Polyp-SAM: Transfer SAM for Polyp Segmentation (paper) |
Code |
202305 |
C. Mattjie et al. |
Exploring the Zero-Shot Capabilities of the Segment Anything Model (SAM) in 2D Medical Imaging: A Comprehensive Evaluation and Practical Guideline (paper) |
None |
202305 |
D. Cheng et al. |
SAM on Medical Images: A Comprehensive Study on Three Prompt Modes (paper) |
None |
202304 |
A. Wang et al. |
SAM Meets Robotic Surgery: An Empirical Study in Robustness Perspective (paper) |
None |
202304 |
Y. Huang et al. |
Segment Anything Model for Medical Images? (paper) |
None |
202304 |
M. Hu et al. |
SkinSAM: Empowering Skin Cancer Segmentation with Segment Anything Model (paper) |
None |
202304 |
B. Wang et al. |
GazeSAM: What You See is What You Segment (paper) |
Code |
202304 |
K. Zhang and D. Liu |
Customized Segment Anything Model for Medical Image Segmentation (paper) |
Code |
202304 |
Z. Qiu et al. |
Learnable Ophthalmology SAM (paper) |
Code |
202304 |
P. Shi et al. |
Generalist Vision Foundation Models for Medical Imaging: A Case Study of Segment Anything Model on Zero-Shot Medical Segmentation (paper) |
None |
202304 |
J. Wu et al. |
Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation (paper) |
Code |
202304 |
J. Ma and B. Wang |
Segment Anything in Medical Images (paper) |
Code |
202304 |
Y. Zhang et al. |
Input Augmentation with SAM: Boosting Medical Image Segmentation with Segmentation Foundation Model (paper) |
None |
202304 |
MA. Mazurowski et al. |
Segment Anything Model for Medical Image Analysis: an Experimental Study (paper) |
Code |
202304 |
S. He et al. |
Accuracy of Segment-Anything Model (SAM) in medical image segmentation tasks (paper) |
None |
202304 |
T. Chen et al. |
SAM Fails to Segment Anything? – SAM-Adapter: Adapting SAM in Underperformed Scenes: Camouflage, Shadow, Medical Image Segmentation, and More (paper) |
Code |
202304 |
C. Hu and X. Li |
When SAM Meets Medical Images: An Investigation of Segment Anything Model (SAM) on Multi-phase Liver Tumor Segmentation (paper) |
None |
202304 |
F. Putz et al. |
The “Segment Anything” foundation model achieves favorable brain tumor autosegmentation accuracy on MRI to support radiotherapy treatment planning (paper) |
None |
202304 |
T. Zhou et al. |
Can SAM Segment Polyps? (paper) |
Code |
202304 |
Y. Liu et al. |
SAMM (Segment Any Medical Model): A 3D Slicer Integration to SAM (paper) |
Code |
202304 |
S. Roy et al. |
SAM.MD: Zero-shot medical image segmentation capabilities of the Segment Anything Model (paper) |
None |
202304 |
S. Mohapatra et al. |
SAM vs BET: A Comparative Study for Brain Extraction and Segmentation of Magnetic Resonance Images using Deep Learning (paper) |
None |
202304 |
R. Deng et al. |
Segment Anything Model (SAM) for Digital Pathology: Assess Zero-shot Segmentation on Whole Slide Imaging (paper) |
None |