Medical Imaging & Computer Vision: AI for Diagnostics and Analysis
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Course Modules:
Module 1: Introduction to Medical Imaging
Types of medical images: X-ray, CT, MRI, ultrasound, PET
Image characteristics and formats (DICOM)
Clinical use cases: cancer detection, fracture localization, organ segmentation
Module 2: Image Processing & Preprocessing
Normalization, resizing, and contrast enhancement
Noise reduction and data augmentation
Anonymizing and handling sensitive patient data
Module 3: Deep Learning for Medical Imaging
CNNs (Convolutional Neural Networks) for image classification
U-Net and segmentation models for tissue/lesion detection
Transfer learning with pre-trained models (e.g., ResNet, DenseNet)
Module 4: Evaluation Metrics & Model Explainability
Sensitivity, specificity, AUC-ROC, Dice coefficient
Visual explanation tools: Grad-CAM, saliency maps
Clinical validation and false positive/negative management
Module 5: Clinical Challenges and Ethical Considerations
Dataset bias and under-representation
Regulatory requirements (FDA, CE) and liability
Human-AI collaboration in diagnosis
Module 6: Capstone Project – Build a Diagnostic Imaging Tool
Use a dataset like ChestX-ray14, COVID-CT, or retinal images
Train a model to classify or segment medical conditions
Submit model report, performance analysis, and ethical review summary
Tools & Technologies Used:
Python
TensorFlow / PyTorch
OpenCV, pydicom, MONAI (for medical imaging)
Matplotlib, Grad-CAM for visualization
Target Audience:
AI and healthcare data science students
Radiologists and clinicians interested in AI support tools
Medical imaging researchers and engineers
Developers building diagnostic assistance applications
Global Learning Benefits:
Understand how to interpret and enhance medical imaging data
Apply computer vision to real-world healthcare challenges
Learn techniques for image classification, segmentation, and anomaly detection
Build trustable, explainable, and regulation-ready imaging AI tools
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