Learnable Ophthalmology SAM
Added some learnable layers for eye disease diagnosis
Segment anything, from space?
In this work, it examines whether SAM’s impressive performance extends to overhead imagery problems. "SAM performs very poorly on both Road Segmentation and Parcel Delineation. It is also notable that the Parcel Delineation problem is challenging overall; "
Generalist Vision Foundation Models for Medical Imaging: A Case Study of Segment Anything Model on Zero-Shot Medical Segmentation
It examines the recent Segment Anything Model (SAM) on medical images, covering various imaging modalities.（OCT/MRI/CT）
Application of Segment Anything Model for Civil Infrastructure Defect Assessment
This research assesses the performance of two deep learning models, SAM and U-Net, for detecting cracks in concrete structures.
Segment Anything in Medical Images
Fine-tuning SAM for medical image segmentation. We freeze the image encoder and prompt encoder and only fine-tune the mask decoder.
When SAM Meets Medical Images: An Investigation of Segment Anything Model (SAM) on Multi-phase Liver Tumor Segmentation
Input Augmentation with SAM: Boosting Medical Image Segmentation with Segmentation Foundation Model
Track Anything: Segment Anything Meets Videos[paper]
SAM Fails to Segment Anything? – SAM-Adapter: Adapting SAM in Underperformed Scenes: Camouflage, Shadow, and More
Inpaint Anything: Segment Anything Meets Image Inpainting
Segment Anything Is Not Always Perfect:An Investigation of SAM on Different Real-world Applications [paper]