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A framework for data augmentation for 2D and 3D image classification and segmentation
This is an official implementation for "SAM-Swin: SAM-Driven Dual-Swin Transformers with Adaptive Lesion Enhancement for Laryngo-Pharyngeal Tumor Detection"
This is an official implementation for "Swin Transformer: Hierarchical Vision Transformer using Shifted Windows".
Advanced AI Explainability for computer vision. Support for CNNs, Vision Transformers, Classification, Object detection, Segmentation, Image similarity and more.
A simple version of CV/Resume for application of Master/Doctoral degree.
Adapting Segment Anything Model for Medical Image Segmentation
Pytorch reimplementation of the Vision Transformer (An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale)
SAM-FNet: SAM-Guided Fusion Network for Laryngo-Pharyngeal Tumor Detection
The implementation of the technical report: "Customized Segment Anything Model for Medical Image Segmentation"
DLGNet: A dual-branch lesion-aware network with the supervised Gaussian Mixture model for colon lesions classification in colonoscopy images (MedIA)
🤗 PEFT: State-of-the-art Parameter-Efficient Fine-Tuning.