Deep Learning for Healthcare Image Analysis
Presented by SGInnovate, NVIDIA & NSCC
SGInnovate, together with the NVIDIA Deep Learning Institute (DLI) and National Supercomputing Centre (NSCC) is proud to bring to you Deep Learning for Healthcare Image Analysis.
Led by seasoned instructors, you can expect to learn the latest deep learning techniques for designing and deploying neural network-powered machine learning across a variety of application domains.
This workshop teaches you to apply deep learning to radiology and medical imaging. The workshop starts with image segmentation which is a technique to classify each pixel into a specific class, followed by training Convolutional Neural Networks (CNNs) to infer the volume of the left ventricle of the human heart from time-series MRI data and concludes by teaching techniques to use radiomics (imaging genomics) to identify the genomics of a disease. Ideally, a student who enrols in this course should have a basic familiarity with deep neural networks, either through the DLI Fundamentals of Computer Vision course or another online training program. Basic coding experience in Python or a similar language is also useful.
In the course, participants will learn to:
- Perform image segmentation on MRI images to determine the location of the left ventricle
- Calculate ejection fractions by measuring differences between diastole and systole using CNNs applied to MRI scans to detect heart disease
- Apply CNNs to MRI scans of low-grade gliomas (LGGs) to determine 1p/19q chromosome co-deletion status
Click here for more SGInnovate – NVIDIA Training Programmes.
08:45am – 09:00am: Registration and Introductions
09:00am – 11:00am: Image Segmentation
Learn the techniques for placing each pixel of an image into a specific class
- Extending Caffe with custom Python layers
- Implementing the process of transfer learning
- Creating fully Convolutional Neural Networks (CNNs) from popular image classification networks
11:00am – 11:15am: Tea Break
11:15am – 01:15pm: Image Analysis
Leverage CNNs for medical image analysis to infer patient status from non-visible images. Train a CNN to infer the volume of the left ventricle of the human heart from time-series MRI data
- Extend a canonical 2D CNN to more complex data
- Use the framework MXNet through the standard Python API and through
- Process high-dimensionality imagery that may be volumetric and have a temporal component
01:15pm – 02:15pm: Lunch
02:15pm – 04:15pm: Image Classification with TensorFlow
Learn about the work being performed at the Mayo Clinic, using deep learning techniques to detect Radiomics from MRI imaging that has led to more effective treatments and better health outcomes for patients with brain tumours
- Designing and training Convolutional Neural Networks (CNNs)
- Using imaging genomics (radiomics) to create biomarkers that identify the genomics of a disease without the use of an invasive biopsy
- Exploring the radiogenomics work being done at the Mayo Clinic
04:15pm – 04:30pm: Closing Comments and Questions
A quick overview of the next - steps you could leverage to build and deploy your own applications and any Q&A
- Wrap-up with the potential next steps and Q&A
- Workshop survey
SGD$500/pax (before GST)
For group discounts or enquiries, please contact [email protected].