Medical Image Analysis with Deep Learning


Embark on a transformative learning journey in the field of Artificial Intelligence (AI) with our specialized training program focused on medical image analysis. Elevate your knowledge, upskill, and stay relevant in this rapidly evolving industry, where the processing and analysis of medical images are anticipated to reach unprecedented heights, with a projected market value of ~$4 billion by 2020. Discover the power of deep learning, a cutting-edge technology that enables the identification, classification, and quantification of patterns in medical images without the need for domain-specific knowledge. This two-day training program covers fundamental image processing techniques, diverse methods of feature extraction, and deep learning techniques such as Autoencoders, Convolutional Neural Networks (CNN), and Recurrent Neural Networks (RNN). Dive into real-world applications of these techniques in medical image analysis, encompassing X-ray, OCT, retinal images, brain images, and more. By acquiring these essential skills, you can kickstart a rewarding career in the dynamic field of medical imaging analysis, utilizing both classical techniques and the latest advancements in deep learning. Your newfound expertise will enable you to contribute to technological innovations, ensuring their seamless integration into high-throughput clinical practices. Join us on this educational journey, where you’ll not only gain valuable insights but also pave the way for impactful contributions to the future of medical imaging.

Course Objectives:

  • Different Medical Image Modalities (X-rays, Magnetic Resonance, Ultrasonic, etc.)
  • Basic Image operation with Python
  • Texture in Medical Images and Classical Feature extraction
  • Neural Network, Autoencoder (Sparse and de-noising)
  • Deep Learning with Convolutional Neural Network (CNN)
  • Application of Deep Learning to Medical Image Analysis


7 hours, 1 Day Course

Mode of Delivery

Classroom-based, Instructor-led Training

Course Outlines

  1. Introduction to Medical Images
    1. X-ray and CT Imaging
    2. Magnetic Resonance Imaging
    3. Ultrasound Imaging
    4. Optical Microscopy and Molecular Imaging
  2. Basic Image operation with Python
    1. Read and Display Image
    2. Covert Colour Image to Grayscale Image
    3. Cropping and Resizing an Image
    4. Rotating Image
    5. Histogram Equalization
    6. Blurring an Image
  3. Texture in Medical Images
    1. Texture characterization – Statistical vs Structural
    2. Co-occurrence Matrix
    3. Orientation Histogram
    4. Local Binary Pattern (LBP)
    5. Texture from Fourier features
    6. Wavelets
    7. Feature extractions for Image (Medical/General)
  4. Neural Network for Visual Computing
    1. Simple Neuron
    2. Neural Network formulation
    3. Learning with Error Propagation
    4. Gradient Checking and Optimization
  5. Deep Learning
    1. What is Deep Learning?
    2. Families of Deep Learning
    3. Multilayer Perceptron
    4. Learning Rule
    5. Autoencoders
    6. Retinal Vessel Detection using Autoencoders
  6. Stacked, Sparse, Denoising Autoencoders
    1. Stacking Autoencoders
    2. Ladder wise pre-training and End-to-End Pre-training
    3. Denoising and Sparse Autoencoders
    4. Ladder Training
    5. End-to-End Training
    6. Medical Image classification with Stacked Autoencoders
  7. Convolutional Neural Network (ConvNet)
    1. What is ConvNet?
    2. Difference between Fully connected NN and ConvNet.
    3. Stride, Padding, and Pooling
    4. Deconvolution
    5. ReLU Transfer Function
  8. Image Classification with CNN
    1. Convolutional Autoencoder
    2. LeNet for Image Classification
    3. AlexNet for Image Classification
  9. Improving Deep Neural Network
    1. Batch Normalization, Dropout
    2. Tuning Hyper-parameters to improve performance of NN.
    3. Learning Rate Annealing
    4. Different Cost Functions
  10. Deep CNN and its application to Medical Images
    1. Vgg16, ResNet34, GooleNet, and DenseNet121
    2. Transfer Learning
    3. Pneumonia detection from Chest X-rays with Deep CNN.
    4. White blood cell classification with CNN
  11. Object Localization
    1. Activation pooling for object localization
    2. Region proposal Network
    3. Sematic segmentation
    4. UNet
    5. Retinopathy Image segmentation with UNet
  12. Spatio-Temporal Deep Learning
    1. Understanding Video analysis
    2. Recurrent Neural Network (RNN)
    3. Long Short Term Memory (LSTM)
    4. Activity recognition using 3D-CNN
    5. Analysis of Brain Images