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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