Deep Learning with PyTorch for Medical Image Analysis

Collection of scripts from the Udemy Deep Learning with PyTorch for Medical Image Analysis course

The PyTorch code for the publication "Quantifying Predictive Uncertainty in Medical Image Analysis with Deep Kernel Learning" on ICHI 2021 (9th IEEE International Conference on Healthcare Informatics)

A deep learning-based system for real-time crowd density analysis and risk detection. Using a multi-column CNN model, it estimates crowd density from images and assists in allocating security and medical resources. Built with PyTorch and Streamlit, it helps prevent overcrowding, manage events, and enhance public safety efficiently.

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This is a repository of deep learning Pytorch code applied to medical imaging. I wrote it following the udemy course called, Deep Learning with PyTorch for Medical Image Analysis. To learn more about this topic please checkout the course, it's really great, thanks.

Cloud-powered AI application that analyzes chest X-rays using deep learning. Built with Streamlit, PyTorch, and Firebase Cloud Storage for real-time, secure, and scalable medical image analysis.

DocGPT (Doctor GPT) is an advanced medical diagnosis system that combines Vision Transformer (ViT) based deep learning models with LangChain agents to provide comprehensive medical image analysis and detailed diagnostic reports. The system leverages the power of PyTorch for deep learning and Groq's LLM for generating human-like medical insights.

No description available

This is a repository of deep learning Pytorch code applied to medical imaging. I wrote it following the udemy course called, Deep Learning with PyTorch for Medical Image Analysis. To learn more about this topic please checkout the course, it's really great, thanks.

No description available

Deep Learning with PyTorch for Medical Image Analysis

Foundations for Deep Learning for Medical Image Analysis with PyTorch

Udemy Course: Deep Learning with PyTorch for Medical Image Analysis

Deep Learning with PyTorch for Medical Image Analysis. Using Pytorch-Lightning to solve real world medical imaging tasks!

CNN to classifiy pneumonia from my Deep Learning with PyTorch for Medical Image Analysis course.

This is the repository for the course Deep Learning with PyTorch for Medical Image Analysis in Udemy

🦴 Deep Learning for Bone Fracture Detection & Classification using FracAtlas Dataset | Medical Image Analysis with TensorFlow/PyTorch

https://nlbsg.udemy.com/course/deep-learning-with-pytorch-for-medical-image-analysis/learn/lecture/29067076#overview

Clinical decision support system for chest X-ray analysis with explainable AI. Deep learning + Grad-CAM for transparent medical diagnosis. PyTorch | Medical Imaging | DICOM

🧠 Detect brain tumors in MRI images using a deep learning model with PyTorch for accurate and efficient medical analysis.

A deep learning pipeline using PyTorch to detect metastatic cancer in histopathology image patches. Implements CNN models for medical image analysis with data augmentation and GPU acceleration.

A deep learning-powered web application for classifying chest X-ray images into four categories: Normal, Pneumonia, Tuberculosis, and Non-X-ray images. Built with PyTorch and Streamlit for real-time medical image analysis.

Deep learning tools for 3D medical image analysis and segmentation using PyTorch & TensorFlow. Supports MRI, CT, and ultrasound data with preprocessing, data loaders, training scripts, and evaluation metrics for automated analysis and segmentation tasks.

This project integrates deep learning models with PyTorch, using SSIM for image quality assessment and pixel density analysis for precision. Key dependencies include TensorFlow, OpenCV, and scikit-learn, enabling a comprehensive approach to tumor detection and medical image analysis.

Deep learning project for classifying skin disease images into 22 categories using PyTorch. Compares MLP, CNN, AlexNet, and VGG16 models with preprocessing, augmentation, and evaluation. Focuses on improving diagnostic support through automated medical image analysis.

Deep learning project using CNNs with TensorFlow/PyTorch and transfer learning (ResNet, EfficientNet) to classify chest X-ray images. Includes Grad-CAM visualizations for interpretability and deployment as a REST API for real-time medical scan analysis.

Deep learning project using CT scans for liver cancer analysis. VGG16 is used to classify patients as healthy or affected, while U-Net performs precise tumor segmentation. Built with PyTorch for medical image processing.

Deep learning-based skin cancer detection system using CNNs to classify dermoscopic images into benign and malignant categories. Includes preprocessing, data augmentation, model training, and evaluation. Built with Python and frameworks like TensorFlow/PyTorch for medical image analysis.

Deep learning project for multi-class diabetic retinopathy classification using PyTorch. Implements a custom CNN and fine-tuned ResNet-50 with transfer learning, data augmentation, and preprocessing. Achieves 82.40% accuracy and 0.9384 ROC-AUC, demonstrating strong performance in medical image analysis.