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Image Classification using Graph Neural Network and Multiscale Wavelet Superpixels
On the contrary, this study investigates image classification using graphs generated from an image-specific number of multiscale superpixels.
Learning from multiscale wavelet superpixels using GNN with spatially heterogeneous pooling
In this study, we investigate learning from a new principled representation in which individual images are represented by an image-specific number of multiscale superpixels.
Computational model discovery with reinforcement learning
The combination of reinforcement learning with objectives (i), (ii) and (iii) differentiate our work from previous modeling attempts based on machine learning.
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.
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