Generation of 3D/ 2D attention maps for both classification and segmentation
- Updated
Jul 17, 2025 - Python
Generation of 3D/ 2D attention maps for both classification and segmentation
📦 PyTorch based visualization package for generating layer-wise explanations for CNNs.
Going deeper into Deep CNNs through visualization methods: Saliency maps, optimize a random input image and deep dreaming with Keras
Code for the paper : "Weakly supervised segmentation with cross-modality equivariant constraints", available at https://arxiv.org/pdf/2104.02488.pdf
Deep Learning Breast MRI Segmentation and Classification
Deep Learning for SAR Ship classification: Focus on Unbalanced Datasets and Inter-Dataset Generalization
First position in Gran Canary Datathon 2021
We will build and train a Deep Convolutional Neural Network (CNN) with Residual Blocks to detect the type of scenery in an image. In addition, we will also use a technique known as Gradient-Weighted Class Activation Mapping (Grad-CAM) to visualize the regions of the inputs and help us explain how our CNN models think and make decision.
Distinguishing Natural and Computer-Generated Images using Multi-Colorspace fused EfficientNet
Deep learning pipeline for classification of Cataract, Diabetic Retinopathy, Glaucoma and Normal using fundus images
Curso de Redes Neuronales Convolucionales con PyTorch
Heat Map 🔥 Generation codes for using PyTorch and CAM Localization Algorithm.
PyTorch MobileNetV2 Stanford Cars Dataset Classification (0.85 Accuracy)
AI-powered skin disease classification system using a fine-tuned ResNet CNN with Grad-CAM explainability. Built with PyTorch and Streamlit to predict 8 skin conditions and visualize model attention for transparent, confidence-aware predictions.
Intracerebral Hemorrhage Detection on Computed Tomography Images Using a Residual Neural Network
Repository of the course project of CMU 16-824 Visual Learning and Recognition
Generate explanations for the ResNet50 classification using Grad-CAM and LIME (XAI Method)
A deep learning-powered medical diagnosis system that detects Pneumonia from chest X-rays and Brain Tumors from MRI scans using two trained CNN models. Includes a FastAPI backend and Streamlit UI for real-time predictions. Built for practical deployment with explainability (Grad-CAM) and modular architecture.
Detection and localization of COVID-19 on chest X-rays
Develop and train image classification models using advanced deep learning techniques to identify diseases specific to apples.
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