What will you in Deep Learning with PyTorch for Medical Image Analysis Course
Understand deep learning principles and their application in medical imaging.
Implement CNNs using PyTorch for tasks like classification, segmentation, and detection.
Work with DICOM, NIfTI, and other medical imaging formats.
Apply pre-processing and augmentation techniques to medical datasets.
Build full pipelines for medical image analysis using real-world examples.
Program Overview
Module 1: Introduction to Deep Learning & PyTorch
⏳ 45 minutes
Overview of deep learning basics and medical imaging context.
PyTorch setup, tensors, and basic operations.
Module 2: Medical Imaging Formats & Preprocessing
⏳ 60 minutes
Introduction to DICOM, NIfTI, and data loading with
pydicom,nibabel.Image normalization, resizing, and augmentation for medical datasets.
Module 3: Convolutional Neural Networks (CNNs)
⏳ 60 minutes
Building basic CNN models in PyTorch.
Activation functions, pooling, and backpropagation.
Module 4: Classification with CNNs in Medical Imaging
⏳ 75 minutes
Training models for binary and multi-class classification.
Evaluation metrics like accuracy, AUC, and confusion matrix.
Module 5: Semantic Segmentation
⏳ 90 minutes
U-Net and encoder-decoder architectures.
Implementing pixel-level segmentation for medical scans.
Module 6: Object Detection & Localization
⏳ 60 minutes
Bounding box techniques in medical imaging.
Integrating detection with classification for full pipelines.
Module 7: Model Evaluation & Optimization
⏳ 45 minutes
Loss functions, overfitting control, and model regularization.
Saving models, checkpointing, and performance tuning.
Module 8: Project: End-to-End Medical Image Analysis
⏳ 90 minutes
Applying learned techniques on a real dataset.
Full workflow: loading → training → evaluation → inference.
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Job Outlook
High Demand: Medical AI is growing in diagnostics, radiology, and pathology.
Career Advancement: Prepares learners for roles in medical AI, computer vision, and research.
Salary Potential: AI roles in healthcare offer $90K–$150K+, especially with domain expertise.
Freelance Opportunities: Opportunities in building models for hospitals, startups, and academic projects.
Explore More Learning Paths
Advance your PyTorch and medical imaging skills with these carefully curated programs designed to help you develop deep learning models for real-world healthcare applications.
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Specification: Deep Learning with PyTorch for Medical Image Analysis Course
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