This Repo containes the implemnetation of generating Guided-GradCAM for 3D medical Imaging using Nifti file in tensorflow 2.0. Different input files can be used in that case need to edit the input to the Guided-gradCAM model.

This repo contains Grad-CAM for 3D volumes.

Deep 3D CNNs for MRI Classification with Alzheimer's Disease And Grad-CAM for Visualization

3D-ResNeXt101 with Grad-CAM Demo. (Pytorch)

Here we introduce the problem of 3D solids classification with a CNN (spheres and octahedra). We implemented a 3D GradCam model, in order to underline the most relevant 3D volumes useful for classification.

Early detection of Alzheimer’s Disease using 3D CNNs on structural MRIs, leveraging the ADNI dataset, Clinica preprocessing, and MeluXina HPC for high-performance training and Grad-CAM interpretability. 🧠

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A 3D medical image segmentation explainability framework with Grad-CAM and volume rendering.

A deep learning system for recognizing handwritten Amharic (Ethiopic) characters, featuring a custom CNN architecture, Grad-CAM explainability, and an interactive 3D neural network visualization built with Three.js.

This project leverages deep convolutional neural networks to automatically classify the severity of knee osteoarthritis from X-ray images using the Kellgren-Lawrence (KL) grading system. It features a Streamlit-based web app with Grad-CAM visualizations, 3D knee anatomy viewer, DeepSeek R1 chatbot for patient Q&A, and hospital recommendations.

Developed a 3D CT analysis pipeline using LUNA16 to study lung nodule candidate detection and explainability in medical AI. Focused on preprocessing, volumetric modeling, uncertainty-aware outputs, and evaluation of Grad-CAM limitations.

A Multimodal Clinical Decision Support System combining 3D MRI Segmentation (Residual Attention U-Net), X-Ray Classification with Explainability (Grad-CAM), and Clinical NLP (BioBERT) into a unified, Dockerized platform

The brain tumor detection project used a CNN-LSTM model to classify MRI images into four categories. CNNs extracted features, LSTMs captured patterns, and Grad-CAM provided interpretability. Preprocessing included resizing and normalization, with explorations into 3D CNNs and attention mechanisms for improvement.

se evaluó el desarrollo de Sistemas Inspectores Explicables que integran técnicas de IA con el objetivo de validar la composición volumétrica con la obtención de parámetros, aumentando la transparencia y la confianza, con métodos XAI post-hoc (Grad-CAM) y arquitecturas intrínsecamente interpretables (MONAILabel, DeepEdit, Segmentación 3D)

实现3d数据的grad_cam可视化

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Alzheimer’s Disease Detection using 3D CNN on MRI scans with Grad-CAM

A project that is showing a demo of exercise recognition based on 3D-CONV with heatmap visualization (grad-CAM).

Developed a deep learning pipeline with MRI preprocessing, 3D CNN/UNet, and Explainable AI (Grad-CAM, SHAP) to improve diagnostic accuracy.

Decodes cognitive states from 4D fMRI volumetric data using 3D CNNs. Features Explainable AI (3D Grad-CAM) to visualize functional brain segregation and temporal dynamics analysis.

Este trabajo clasifica estados de dolor crónico en fMRI de rata mediante una red VGG16 3D e integra Grad-CAM, Grad-CAM++, Respond-CAM y HiResCAM para analizar regiones relevantes. Se incorpora la métrica coverage para validar activaciones y se comparan el rendimiento y la estabilidad de los métodos de interpretabilidad.

A CNN-based image classification system with real-time explainability, featuring feature map visualization, Grad-CAM heatmaps, and interactive 3D neural network representation.

Deep learning models for Alzheimer's Disease classification using 3D CNNs and Grad-CAM on MRI, and temporal models on longitudinal CSF biomarkers.

Explainable AI platform that visualizes real neural network activations on an MRI-based 3D anatomical brain using YOLOv10, CLIP, Grad-CAM, and cortex mapping.

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A leakage-free ML pipeline for early pancreatic cancer detection from CT scans — combining handcrafted radiomics (PyRadiomics, ComBat) with volumetric deep learning (3D ResNet-10, MONAI) and explainable AI (SHAP, Grad-CAM).

Spatio-Temporal Explainability for 3D CNNs using R3D-18 on UCF101. Includes Grad-CAM and Integrated Gradients for video, dataset-level faithfulness evaluation (deletion AUC), sanity checks, and counterfactual temporal analysis. A structured study of video model interpretability.

I work on deep learning models that identify AI-generated images using CNNs, 3D-CNNs, and interpretable AI. My projects focus on binary classification of real vs synthetic imagery, dataset engineering, and model transparency through XAI (Grad-CAM).

MedAI-3D-CT-Scan-System is an AI-based application for detecting lung nodules from 3D chest CT images. It uses deep learning models to analyze scans and Grad-CAM for explainability. The system helps doctors identify abnormalities early, improving accuracy, reducing workload, and supporting better diagnosis in healthcare.

Floraverse is an AI Ayurvedic platform with three modules: "The Eye" (EfficientNetB0 plant identification, 53 classes, Grad‑CAM explainability), "The Vaidya" (FastAPI remedy backend with 700+ herb database), and "The Interface" (React frontend with 3D garden). Model achieves ~83% accuracy.