Learning Representations for Medical Image Diagnosis: Impact of Curriculum Training and Architectural Design
This talk summarizes the main outcomes of my PhD thesis. We investigated two key aspects to learn feature representations leveraging Convolutional Neural Networks from medical images for Computer-Aided Diagnosis tasks. In the first part, we explored the role of architectural design in dealing with spatial information. In the second part, we designed curriculum training strategies to control the order, pace, and number of images presented to the optimizer.
The presentation of my dissertation is available on Youtube.