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Found 5 papers, 2 papers with code
FatNet: High Resolution Kernels for Classification Using Fully Convolutional Optical Neural Networks
This paper describes the transformation of a traditional in-silico classification network into an optical fully convolutional neural network with high-resolution feature maps and kernels.
Ranked #180 on
Image Classification
on CIFAR-100
Contrastive Counterfactual Visual Explanations With Overdetermination
A novel explainable AI method called CLEAR Image is introduced in this paper.
The relationship between Fully Connected Layers and number of classes for the analysis of retinal images
This paper experiments with the number of fully-connected layers in a deep convolutional neural network as applied to the classification of fundus retinal images.
Detection of Pitt-Hopkins Syndrome based on morphological facial features
The features are used to investigate the statistical differences between the two populations of PTHS and healthy controls.
Generating Magnetic Resonance Spectroscopy Imaging Data of Brain Tumours from Linear, Non-Linear and Deep Learning Models
Magnetic Resonance Spectroscopy (MRS) provides valuable information to help with the identification and understanding of brain tumors, yet MRS is not a widely available medical imaging modality.
