Copertina

Assessing the generalization performance of SAM for ureteroscopy scene understanding

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Villagrana, Martin, Lopez-Tiro, Francisco, Larose, Clement, Ochoa-Ruiz, Gilberto, Daul, Christian
Natura:	Preprint
Pubblicazione:	2025
Soggetti:	Image and Video Processing Artificial Intelligence Computer Vision and Pattern Recognition Machine Learning
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Boosting Kidney Stone Identification in Endoscopic Images Using Two-Step Transfer Learning
di: Lopez-Tiro, Francisco, et al.
Pubblicazione: (2022)

Multi-Scale Structural-aware Exposure Correction for Endoscopic Imaging
di: Garcia-Vega, Axel, et al.
Pubblicazione: (2022)

MedicoSAM: Robust Improvement of SAM for Medical Imaging
di: Archit, Anwai, et al.
Pubblicazione: (2025)

Is SAM 2 Better than SAM in Medical Image Segmentation?
di: Sengupta, Sourya, et al.
Pubblicazione: (2024)

PA-SAM: Prompt Adapter SAM for High-Quality Image Segmentation
di: Xie, Zhaozhi, et al.
Pubblicazione: (2024)

Ultrasound SAM Adapter: Adapting SAM for Breast Lesion Segmentation in Ultrasound Images
di: Tu, Zhengzheng, et al.
Pubblicazione: (2024)

De-LightSAM: Modality-Decoupled Lightweight SAM for Generalizable Medical Segmentation
di: Xu, Qing, et al.
Pubblicazione: (2024)

PathSeqSAM: Sequential Modeling for Pathology Image Segmentation with SAM2
di: Zhu, Mingyang, et al.
Pubblicazione: (2025)

Path-SAM2: Transfer SAM2 for digital pathology semantic segmentation
di: Zhang, Mingya, et al.
Pubblicazione: (2024)

Comparing SAM 2 and SAM 3 for Zero-Shot Segmentation of 3D Medical Data
di: Chakrabarty, Satrajit, et al.
Pubblicazione: (2025)

SAM2-3dMed: Empowering SAM2 for 3D Medical Image Segmentation
di: Yang, Yeqing, et al.
Pubblicazione: (2025)

Seismic Fault SAM: Adapting SAM with Lightweight Modules and 2.5D Strategy for Fault Detection
di: Chen, Ran, et al.
Pubblicazione: (2024)

CRC-SAM: SAM-Based Multi-Modal Segmentation and Quantification of Colorectal Cancer in CT, Colonoscopy, and Histology Images
di: Lao, Daniel
Pubblicazione: (2026)

ExploreGS: a vision-based low overhead framework for 3D scene reconstruction
di: Feng, Yunji, et al.
Pubblicazione: (2025)

SAM2CLIP2SAM: Vision Language Model for Segmentation of 3D CT Scans for Covid-19 Detection
di: Kollias, Dimitrios, et al.
Pubblicazione: (2024)

TAGS: 3D Tumor-Adaptive Guidance for SAM
di: Li, Sirui, et al.
Pubblicazione: (2025)

GBT-SAM: A Parameter-Efficient Depth-Aware Model for Generalizable Brain tumour Segmentation on mp-MRI
di: Diana-Albelda, Cecilia, et al.
Pubblicazione: (2025)

nnSAM2: nnUNet-Enhanced One-Prompt SAM2 for Few-shot Multi-Modality Segmentation and Composition Analysis of Lumbar Paraspinal Muscles
di: Zhang, Zhongyi, et al.
Pubblicazione: (2025)

DB-SAM: Delving into High Quality Universal Medical Image Segmentation
di: Qin, Chao, et al.
Pubblicazione: (2024)

UN-SAM: Universal Prompt-Free Segmentation for Generalized Nuclei Images
di: Chen, Zhen, et al.
Pubblicazione: (2024)

SAM3D: Segment Anything Model in Volumetric Medical Images
di: Bui, Nhat-Tan, et al.
Pubblicazione: (2023)

Segment anything model (SAM) for brain extraction in fMRI studies
di: Chenna, Dwith, et al.
Pubblicazione: (2024)

DeSAM: Decoupled Segment Anything Model for Generalizable Medical Image Segmentation
di: Gao, Yifan, et al.
Pubblicazione: (2023)

UniUltra: Interactive Parameter-Efficient SAM2 for Universal Ultrasound Segmentation
di: Li, Yue, et al.
Pubblicazione: (2025)

Assessing Foundational Medical 'Segment Anything' (Med-SAM1, Med-SAM2) Deep Learning Models for Left Atrial Segmentation in 3D LGE MRI
di: Mehrnia, Mehri, et al.
Pubblicazione: (2024)

Talk2SAM: Text-Guided Semantic Enhancement for Complex-Shaped Object Segmentation
di: Vetoshkin, Luka, et al.
Pubblicazione: (2025)

SemiSAM+: Rethinking Semi-Supervised Medical Image Segmentation in the Era of Foundation Models
di: Zhang, Yichi, et al.
Pubblicazione: (2025)

nnSAM: Plug-and-play Segment Anything Model Improves nnUNet Performance
di: Li, Yunxiang, et al.
Pubblicazione: (2023)

Technical Report for ReID-SAM on SkiTB Visual Tracking Challenge 2025
di: Li, Kunjun, et al.
Pubblicazione: (2025)

Temporal-spatial Adaptation of Promptable SAM Enhance Accuracy and Generalizability of cine CMR Segmentation
di: Chen, Zhennong, et al.
Pubblicazione: (2024)

Towards Collective Intelligence: Uncertainty-aware SAM Adaptation for Ambiguous Medical Image Segmentation
di: Jiang, Mingzhou, et al.
Pubblicazione: (2024)

Exploiting the Segment Anything Model (SAM) for Lung Segmentation in Chest X-ray Images
di: de Carvalho, Gabriel Bellon, et al.
Pubblicazione: (2024)

MoSAM: Motion-Guided Segment Anything Model with Spatial-Temporal Memory Selection
di: Yang, Qiushi, et al.
Pubblicazione: (2025)

SAM & SAM 2 in 3D Slicer: SegmentWithSAM Extension for Annotating Medical Images
di: Yildiz, Zafer, et al.
Pubblicazione: (2024)

subCellSAM: Zero-Shot (Sub-)Cellular Segmentation for Hit Validation in Drug Discovery
di: Hanimann, Jacob, et al.
Pubblicazione: (2025)

SAM-Guided Robust Representation Learning for One-Shot 3D Medical Image Segmentation
di: Wang, Jia, et al.
Pubblicazione: (2025)

SAM-I2I: Unleash the Power of Segment Anything Model for Medical Image Translation
di: Huo, Jiayu, et al.
Pubblicazione: (2024)

Vision Transformers for Kidney Stone Image Classification: A Comparative Study with CNNs
di: Reyes-Amezcua, Ivan, et al.
Pubblicazione: (2025)

FedAgain: A Trust-Based and Robust Federated Learning Strategy for an Automated Kidney Stone Identification in Ureteroscopy
di: Reyes-Amezcua, Ivan, et al.
Pubblicazione: (2026)

FastSAM3D: An Efficient Segment Anything Model for 3D Volumetric Medical Images
di: Shen, Yiqing, et al.
Pubblicazione: (2024)