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Found 5 papers, 2 papers with code
Spectrum Translation for Refinement of Image Generation (STIG) Based on Contrastive Learning and Spectral Filter Profile
We evaluate our framework across eight fake image datasets and various cutting-edge models to demonstrate the effectiveness of STIG.
Attention Guided CAM: Visual Explanations of Vision Transformer Guided by Self-Attention
This approach of our method provides elaborate high-level semantic explanations with great localization performance only with the class labels.
Atlas Based Segmentations via Semi-Supervised Diffeomorphic Registrations
Whereas supervised segmentation methods only automate the segmentation process for a select few number of OARs, we demonstrate that our methods can achieve similar performance for OARs of interest, while also providing segmentations for every other OAR on the provided atlas.
Machine Learning Techniques for Biomedical Image Segmentation: An Overview of Technical Aspects and Introduction to State-of-Art Applications
In recent years, significant progress has been made in developing more accurate and efficient machine learning algorithms for segmentation of medical and natural images.
Modified U-Net (mU-Net) with Incorporation of Object-Dependent High Level Features for Improved Liver and Liver-Tumor Segmentation in CT Images
To cope with these problems, we added a residual path with deconvolution and activation operations to the skip connection of the U-Net to avoid duplication of low resolution information of features.
