CT scan machine learning models including AxialNet and HiResCAM
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Updated
Dec 8, 2022 - Python
CT scan machine learning models including AxialNet and HiResCAM
introducing tools for deep learning in medicine
Computer vision visualization such as Grad-CAM, etc.
Propose fully convolutional network with skip connection which is deeper than the network used in vanilla DQN.
Research on AutoML and Explainability.
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'
Code for IMVIP 2024 paper "Analysing the Impact of Pre-training in ResUNet Architectures for Multiple Sclerosis Lesion Segmentation using EigenGradCAM"
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…
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
Framework for benchmarking black-box adversarial attacks, modeled with ES.
Interactive visualization of classification model's decisions (Using Grad-CAM theorem and Animint2 R package), which can help researches understand mechanisms of computer vision models' decisions
This repository is the code basis for the paper titled "Balancing Privacy and Explainability in Federated Learning"
Gradient Class Activation Map (with pytorch): Visualize the model's prediction to help understand CNN and ViT models better
Grad-CAM Implementation in PyTorch
[a.a. 22/23] G. Antonucci, N. Pagliara
Chest X-Ray COVID-19 Detection. Work presented at the Ethics and Explainability for Responsible Data Science (EE-RDS 2021) Conference. IEEE Paper: https://ieeexplore.ieee.org/document/9708580
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