Dr Maria Valdes Hernandez lecture "Deep Learning in Medical Imaging"
Dr Maria Valdes Hernandez gave a lecture in Deep Learning in Medical Imaging, which is available to view on the Edinburgh Imaging site.
At the recent “Medical Image Analysis: application to brain” course, hosted by the University of Florence, Dr Maria Valdes Hernandez, Lecturer in Medical Imaging Analysis delivered her lecture: Deep learning in medical imaging.
The lecture begins by introducing the neuron as the basic computational unit of the brain. Dr Hernandez goes on to describe neuronal connections in neural pathways, or bundles, that are associated with different functions. She elaborates on how artificial neural networks attempt to mimic these natural neural pathways.
Following the introduction, the lecture presents mathematical insights of artificial neural networks and different types of network architectures. Dr Hernandez emphasises those schemes which have been most widely applied in the field of medical image analysis.
Some concepts within the computational jargon of artificial neural networks are also explained:
- cost function or loss
- activation function
- gradient descend, and
The lecture presents and illustrates examples in MATLAB, of the pre-processing steps most commonly needed in brain MR image segmentation and classification tasks. These examples are extensible to other types of images, not just brain images.
The Open Neural Network Exchange (ONNX) format is described, which facilitates conversion between programming languages and platforms (for example, from MATLAB to python and vice versa). How to convert from ONNX format to the desired framework, or how to deploy the code for different platforms, using runtimes designed to accelerate inferencing, are also covered.
Dr Hernandez also explains the principles of convolutional neural networks (CNNs) and the elements involved in their design:
- filter (kernel) size
- number of layers and units
- pooling parameters
- regularisation type, and
- batch size
Generative Adversarial Networks (GANs) are also briefly introduced, as well as finally, different ways of validating the networks’ outputs.
The lecture concludes with some remarks on challenges that the field faces, and a list of platforms and academic journals where most of the advances in the field are published.
The lecture's content is directly applicable to the Edinburgh Imaging Academy’s Imaging MSc / Dip/ Cert, and Applied Medical Image Analysis Cert post-graduate programmes.
Dr Maria Valdes-Hernandez "Deep Leaning in medical Imaging" lecture slides - please, view them here
About Dr Maria Valdes- Hernandez
Dr Maria Valdés Hernández graduated with merits as Electronic Engineer and MSc in Electronics from the Central University of Las Villas, Cuba, and PhD in Electronics and Informatics from Gunma University, Japan. With expertise in software development, image and signal processing and analysis, Dr Valdés Hernández work is focused on developing software tools and pipelines to process and analyse the images for various clinical studies, and works in the discovery of imaging biomarkers of small vessel disease, ageing and neurodegeneration. Maria is currently a Scientific Committee Board Member and the Lecturer in Image Processing and Analysis of The Row Fogo Centre for Research into Ageing and The Brain and Lead academic of the Image Analysis and Image Processing Techniques courses offered by the MSc online Programmes of the Edinburgh Imaging Academy.
Article adapted from Edinburgh Imaging website.
Dr Maria Valdes Hernandez research profile
Dr Maria Valdes Hernandez research profile- Perivascular Spaces in Small Vessel Disease (external website)