saliency map, adversarial image, (gradient) class activation map
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Updated
Oct 4, 2019 - Jupyter Notebook
saliency map, adversarial image, (gradient) class activation map
Distinguishing Natural and Computer-Generated Images using Multi-Colorspace fused EfficientNet
PyTorch Implement of Grad-CAM
Testing Grad-CAM localization ability on brain tumor classification task
CT scan machine learning models including AxialNet and HiResCAM
Exploration of different explainability methods for 'black- box' classification models used for medical diagnosis
On the evaluation of deep learning interpretability methods for medical images under the scope of faithfulness
Deep Learning Project - Convolutional Neural Networks for Brain Tumor Images Classification
A Comprehensive Study on Cloud-Based Model Interpretability, Accountability, and Privacy in Machine Learning with Resilience to Adversarial Attacks
Repository for the journal article 'SHAMSUL: Systematic Holistic Analysis to investigate Medical Significance Utilizing Local interpretability methods in deep learning for chest radiography pathology prediction'
Paper Name: Utilizing Convolutional Neural Networks and Gradient-weighted Class Activation Mapping (Grad-CAM) for Dairy Cow Teat Image Classification. Testing the impact of CNN and Grad-CAM in the accuracy of dairy Cows Teat Imaga classification. Dataset and testing software from: https://github.com/YoushanZhang/SCTL
Gradient-weighted Class Activation Mapping
A convenient and powerful tool written in Pytorch for using Grad-CAM.
Deep Learning for SAR Ship classification: Focus on Unbalanced Datasets and Inter-Dataset Generalization
introducing tools for deep learning in medicine
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