Cover Image

CataractSAM-2: A Domain-Adapted Model for Anterior Segment Surgery Segmentation and Scalable Ground-Truth Annotation

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Eslami, Mohammad, Ganeshkumar, Dhanvinkumar, Kazeminasab, Saber, Morley, Michael G., Boland, Michael V., Lin, Michael M., Miller, John B., Friedman, David S., Zebardast, Nazlee, Sobrin, Lucia, Elze, Tobias
Format:	Preprint
Published:	2026
Subjects:	Computer Vision and Pattern Recognition Artificial Intelligence Databases Machine Learning Robotics
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Training-inference input alignment outweighs framework choice in longitudinal retinal image prediction
by: Chen, Liyin, et al.
Published: (2026)

Harvard Glaucoma Fairness: A Retinal Nerve Disease Dataset for Fairness Learning and Fair Identity Normalization
by: Luo, Yan, et al.
Published: (2023)

Cataract Surgery in a Case of Bilateral Idiopathic Corneal Ascher Rings Associated With Anterior Segment Changes
by: Anca Delia Pantalon
Published: (2026)

Evaluating an Adaptive Multispectral Turret System for Autonomous Tracking Across Variable Illumination Conditions
by: Sachdeva, Aahan, et al.
Published: (2025)

Phase-Informed Tool Segmentation for Manual Small-Incision Cataract Surgery
by: Sachdeva, Bhuvan, et al.
Published: (2024)

Look Ma, No Ground Truth! Ground-Truth-Free Tuning of Structure from Motion and Visual SLAM
by: Fontan, Alejandro, et al.
Published: (2024)

Medical Image Segmentation with SAM-generated Annotations
by: Häkkinen, Iira, et al.
Published: (2024)

Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation
by: Wu, Junde, et al.
Published: (2023)

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

Surgical-DeSAM: Decoupling SAM for Instrument Segmentation in Robotic Surgery
by: Sheng, Yuyang, et al.
Published: (2024)

RefSAM: Efficiently Adapting Segmenting Anything Model for Referring Video Object Segmentation
by: Li, Yonglin, et al.
Published: (2023)

Learning to Adapt SAM for Segmenting Cross-domain Point Clouds
by: Peng, Xidong, et al.
Published: (2023)

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

The engine room of SaaS: leveraging multi-cloud, aiops, and finops for scalable and profitable enterprise data platforms
by: Palanisamy, Ganeshkumar
Published: (2025)

ASLseg: Adapting SAM in the Loop for Semi-supervised Liver Tumor Segmentation
by: Chen, Shiyun, et al.
Published: (2023)

UV-SAM: Adapting Segment Anything Model for Urban Village Identification
by: Zhang, Xin, et al.
Published: (2024)

Federated Learning for Diabetic Retinopathy Diagnosis: Enhancing Accuracy and Generalizability in Under-Resourced Regions
by: Raj, Gajan Mohan, et al.
Published: (2024)

Seeing Heat with Color -- RGB-Only Wildfire Temperature Inference from SAM-Guided Multimodal Distillation using Radiometric Ground Truth
by: Marinaccio, Michael, et al.
Published: (2025)

FairSeg: A Large-Scale Medical Image Segmentation Dataset for Fairness Learning Using Segment Anything Model with Fair Error-Bound Scaling
by: Tian, Yu, et al.
Published: (2023)

MapSAM2: Adapting SAM2 for Automatic Segmentation of Historical Map Images and Time Series
by: Xia, Xue, et al.
Published: (2025)

CataractCompDetect: Intraoperative Complication Detection in Cataract Surgery
by: Sachdeva, Bhuvan, et al.
Published: (2025)

Adapting SAM for Surgical Instrument Tracking and Segmentation in Endoscopic Submucosal Dissection Videos
by: Yu, Jieming, et al.
Published: (2024)

AffordanceSAM: Segment Anything Once More in Affordance Grounding
by: Jiang, Dengyang, et al.
Published: (2025)

MPG-SAM 2: Adapting SAM 2 with Mask Priors and Global Context for Referring Video Object Segmentation
by: Rong, Fu, et al.
Published: (2025)

RefSAM3D: Adapting SAM with Cross-modal Reference for 3D Medical Image Segmentation
by: Gao, Xiang, et al.
Published: (2024)

More than Segmentation: Benchmarking SAM 3 for Segmentation, 3D Perception, and Reconstruction in Robotic Surgery
by: Dong, Wenzhen, et al.
Published: (2025)

Principles of Catastrophic Forgetting for Continual Semantic Segmentation in Automated Driving
by: Kalb, Tobias Michael
Published: (2024)

ClassWise-SAM-Adapter: Parameter Efficient Fine-tuning Adapts Segment Anything to SAR Domain for Semantic Segmentation
by: Pu, Xinyang, et al.
Published: (2024)

Char-SAM: Turning Segment Anything Model into Scene Text Segmentation Annotator with Character-level Visual Prompts
by: Xie, Enze, et al.
Published: (2024)

MapSAM: Adapting Segment Anything Model for Automated Feature Detection in Historical Maps
by: Xia, Xue, et al.
Published: (2024)

Adapting SAM to Nuclei Instance Segmentation and Classification via Cooperative Fine-Grained Refinement
by: Su, Jingze, et al.
Published: (2026)

SAM2-ELNet: Label Enhancement and Automatic Annotation for Remote Sensing Segmentation
by: Yang, Jianhao, et al.
Published: (2025)

Anterior Segment Optical Coherence Tomography and Anterior Segment Optical Coherence Tomography Angiography After Bleb Forming Glaucoma Surgeries: A Systematic Review
by: Martin Kallab, et al.
Published: (2025)

Data-Efficient Surgical Phase Segmentation in Small-Incision Cataract Surgery: A Controlled Study of Vision Foundation Models
by: Spencer, Lincoln, et al.
Published: (2026)

SAM2-Adapter: Evaluating & Adapting Segment Anything 2 in Downstream Tasks: Camouflage, Shadow, Medical Image Segmentation, and More
by: Chen, Tianrun, et al.
Published: (2024)

FS-SAM2: Adapting Segment Anything Model 2 for Few-Shot Semantic Segmentation via Low-Rank Adaptation
by: Forni, Bernardo, et al.
Published: (2025)

Intraoperative Glioma Segmentation with YOLO + SAM for Improved Accuracy in Tumor Resection
by: Kassam, Samir, et al.
Published: (2024)

SU-SAM: A Simple Unified Framework for Adapting Segment Anything Model in Underperformed Scenes
by: Song, Yiran, et al.
Published: (2024)

Beyond Adapting SAM: Towards End-to-End Ultrasound Image Segmentation via Auto Prompting
by: Lin, Xian, et al.
Published: (2023)

Experimental Framework for Generating Reliable Ground Truth for Laryngeal Spatial Segmentation Tasks
by: Ghasemzadeh, Hamzeh, et al.
Published: (2024)