Testing Grad-CAM localization ability on brain tumor classification task
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Updated
May 28, 2023 - Jupyter Notebook
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
saliency map, adversarial image, (gradient) class activation map
Distinguishing Natural and Computer-Generated Images using Multi-Colorspace fused EfficientNet
PyTorch Implement of Grad-CAM
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
Code for IMVIP 2024 paper "Analysing the Impact of Pre-training in ResUNet Architectures for Multiple Sclerosis Lesion Segmentation using EigenGradCAM"
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'
Repository for the 'best student paper award' winning paper at the IEEE 35th International Symposium on Computer Based Medical Systems (CBMS 2022), Exploring LRP and Grad-CAM visualization to interpret multi-label-multi-class pathology prediction using chest radiography, Mahbub Ul Alam, Jón Rúnar Baldvinsson and Yuxia Wang. https://doi.org/10.11…
Develop and train image classification models using advanced deep learning techniques to identify diseases specific to apples.
A binary classification using Convolution Neural Network (CNN, or ConvNet) model.
Computer vision visualization such as Grad-CAM, etc.
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