Advanced AI Explainability for computer vision. Support for CNNs, Vision Transformers, Classification, Object detection, Segmentation, Image similarity and more.

Guided Interpretable Facial Expression Recognition via Spatial Action Unit Cues

COVID-CXNet: Diagnosing COVID-19 in Frontal Chest X-ray Images using Deep Learning. Preprint available on arXiv: https://arxiv.org/abs/2006.13807

DeviceScope: An Interactive App to Detect and Localize Appliance Patterns in Electrical Consumption Time Series

Detecting Severe Malaria Anaemia and investigating the morphological characteristics of red blood cells at its presenc

Enhanced CNN model for malaria cell classification, featuring Class Activation Mapping (CAM) as a non-agnstic technique for anomaly localization and LIME (Local Interpretable-agnostic Explanation) for interpretability, ensuring high accuracy and transparent AI diagnostics.

An awesome list of papers and tools about the class activation map (CAM) technology.

DeepInsight3D package to deal with multi-omics or multi-layered data

A collection of my Jupyter notebooks, showcasing my exploration and learning journey in the field of Computer Vision

Repository for the paper "Neural Networks for Classification and Unsupervised Segmentation of Visibility Artifacts on Monocular Camera Image"

【瑞士军刀般的工具】用最短的代码完成对模型的分析，包含 ImageNet Val、FLOPs、Params、Throuthput、CAM 等

Class Activation Map (CAM and Grad-CAM) Analysis of fine-tuned CNNs with transfer learning for Pokemon classification task to understand the features learned by deep CNN

Deep functional residue identification

Satellite photographs taken by the Sentinel-2 were classified with pre-trained ResNet-50 and VGG16 models. In addition made CAM model.

Code and data for our learning-based eXplainable AI (XAI) method TAME: M. Ntrougkas, N. Gkalelis, V. Mezaris, "TAME: Attention Mechanism Based Feature Fusion for Generating Explanation Maps of Convolutional Neural Networks", Proc. IEEE Int. Symposium on Multimedia (ISM), Naples, Italy, Dec. 2022.

Repository containing code to run Score-CAM algorithm available on https://arxiv.org/pdf/1910.01279v1.pdf.

Code for the paper : "Weakly supervised segmentation with cross-modality equivariant constraints", available at https://arxiv.org/pdf/2104.02488.pdf

This work study the "Activation/Saliency Map" in image classification, which emphasize the regions in a image where model focus on to give the final predication result.

Computer Vision with PyTorch for Medical Image Analysis

Official implementation of Score-CAM in PyTorch