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Instructor-led Classroom Adult Training in Singapore - Modular Fast Track Skill-Based Trainings

Medical Image Analysis with Deep Learning

Processing and analysis of medical images; a rapidly growing industry expected to reach ~$4 billion by 2020. Recent advances in deep learning are helping to identify, classify, and quantify patterns in medical images. Deep Learning helps to exploit hierarchical feature representations learned exclusively from data without proper domain-specific knowledge. Deep learning is rapidly becoming the state of the art in numerous medical applications. This two days training will cover basic image processing techniques, different methods of features extractions, deep learning techniques (Autoencoders, CNN, RNN), and its application to Medical Image analysis (X-ray, OCT, Retinal Images, Brain Images, etc.). This training helps to start a career in the field of medical imaging analysis using Classical techniques and Deep Learning. You can use these skills in order to develop newer technological innovations and regularize them for high-throughput clinical translation and usage.

Course Highlights

  • 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.

Certificate

All participants will receive a Certificate of Completion from Tertiary Courses after achieved at least 75% attendance.

Funding and Grant Applications

Click the links below to apply. Note that you need to register the course first.

For Singaporeans: SkillsFuture Credit

For Company: SSG Training Grant

Course Code: CRS-N-0047074

Course Booking

$598.00 (GST-exclusive)
$639.86(GST-inclusive)

Course Date

Course Time

* Required Fields

Course Cancellation/Reschedule Policy

We reserve the right to cancel or re-schedule the course due to unforeseen circumstances. If the course is cancelled, we will refund 100% to participants.
Note the venue of the training is subject to changes due to class size and availability of the classroom.
Note the minimal class size to start a class is 3 Pax.


Course Details

Day 1

Module 1 Introduction to Medical Images

  • X-ray and CT Imaging
  • Magnetic Resonance Imaging
  • Ultrasound Imaging
  • Optical Microscopy and Molecular Imaging

Module 2 Basic Image operation with Python

  • Read and Display Image
  • Covert Colour Image to Grayscale Image
  • Cropping and Resizing an Image
  • Rotating Image
  • Histogram Equalization
  • Blurring an Image

Module 3 Texture in Medical Images

  • Texture characterization – Statistical vs Structural
  • Co-occurrence Matrix
  • Orientation Histogram
  • Local Binary Pattern (LBP)
  • Texture from Fourier features
  • Wavelets
  • Feature extractions for Image (Medical/General)

Module 4 Neural Network for Visual Computing

  • Simple Neuron
  • Neural Network formulation
  • Learning with Error Propagation
  • Gradient Checking and Optimization

Module 5 Deep Learning

  • What is Deep Learning?
  • Families of Deep Learning
  • Multilayer Perceptron
  • Learning Rule
  • Autoencoders
  • Retinal Vessel Detection using Autoencoders

Module 6 Stacked, Sparse, Denoising Autoencoders

  • Stacking Autoencoders
  • Ladder wise pre-training and End-to-End Pre-training
  • Denoising and Sparse Autoencoders
  • Ladder Training
  • End-to-End Training
  • Medical Image classification with Stacked Autoencoders


Day 2

Module 7 Convolutional Neural Network (ConvNet)

  • What is ConvNet?
  • Difference between Fully connected NN and ConvNet.
  • Stride, Padding, and Pooling
  • Deconvolution
  • ReLU Transfer Function

Module 8 Image Classification with CNN

  • Convolutional Autoencoder
  • LeNet for Image Classification
  • AlexNet for Image Classification

Module 9 Improving Deep Neural Network

  • Batch Normalization, Dropout
  • Tuning Hyper-parameters to improve performance of NN.
  • Learning Rate Annealing
  • Different Cost Functions

Module 10 Deep CNN and its application to Medical Images

  • Vgg16, ResNet34, GooleNet, and DenseNet121
  • Transfer Learning
  • Pneumonia detection from Chest X-rays with Deep CNN.
  • White blood cell classification with CNN

Module 11 Object Localization

  • Activation pooling for object localization
  • Region proposal Network
  • Sematic segmentation
  • UNet
  • Retinopathy Image segmentation with UNet

Module 12 Spatio-Temporal Deep Learning

  • Understanding Video analysis
  • Recurrent Neural Network (RNN)
  • Long Short Term Memory (LSTM)
  • Activity recognition using 3D-CNN
  • Analysis of Brain Images

Course Admin

Prerequisite

This is a intermediate course. The following knowledge is assumed:

  • Basic Python

Software Requirement

Please install the following software prior to the class

1. Pycharm : - Install Pycharm (https://www.jetbrains.com/pycharm/download/)

2 . Install Tensorflow on Mac

Please follow this guide to install Tensorflow on Mac https://www.tensorflow.org/install/install_mac

Alternatively, you can enter the following commands on your Mac terminal

pip3 install tensorflow

3 . Install Tensorflow on Window

Please follow this guide to install Tensorflow on Window https://www.tensorflow.org/install/install_windows

Tensorflow only support Python 3.5, you can install Tensorflow in Python 3.5 the following ways:

Who Should Attend

  • Data Scientists
  • Data Analysts
  • Machine Learning Engineers and Developers

Trainers

Deep Learning TrainerDr. Sudipta Samanta received his Ph.D. for his research in the field of computational Biophysics in 2009. He has more than 9 years research experience as a scientist. His current interests include Machine Learning, Deep Learning, Health Care Data analysis, and computer simulation. He is also the founder of "Baby Lion Organic medicinal Farm".

Customer Reviews (5)

Might Recommend Review by Course Participant/Trainee
1. Do you find the course meet your expectation?
2. Do you find the trainer knowledgeable in this subject?
3. How do you find the training environment
Students should come in the ability to set up a virtual environment and basic python so as to reduce time teaching basic programming concepts.

Trainer was very comprehensive in teaching (Posted on 7/2/2019)
Might Recommend Review by Course Participant/Trainee
1. Do you find the course meet your expectation?
2. Do you find the trainer knowledgeable in this subject?
3. How do you find the training environment
Students should come in the ability to set up a virtual environment and basic python so as to reduce time teaching basic programming concepts.

Trainer was very comprehensive in teaching (Posted on 7/2/2019)
Will Recommend Review by Course Participant/Trainee
1. Do you find the course meet your expectation?
2. Do you find the trainer knowledgeable in this subject?
3. How do you find the training environment
. (Posted on 5/22/2019)
Will Recommend Review by Course Participant/Trainee
1. Do you find the course meet your expectation?
2. Do you find the trainer knowledgeable in this subject?
3. How do you find the training environment
I think we wasted some time with downloading the appropriate packages (eg. keras, torch, fastai etc), so if the pre-course instructions could include these, it will give us more time to focus on the content.

Perhaps have a recap session, Q&A session, or organize a sort of alumni follow up. It is especially useful when one goes back, digest the materials and tried out in earnest all the problems and solutions. I think a small fee for that is acceptable for most of us. (Posted on 5/22/2019)
Will Recommend Review by Course Participant/Trainee
1. Do you find the course meet your expectation?
2. Do you find the trainer knowledgeable in this subject?
3. How do you find the training environment
. (Posted on 4/22/2019)

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