Deep Learning: Modular in Theory, Inflexible in Practice
The high-level view of deep learning is elegant: composing differentiable components together trained in an end-to-end fashion. Deep learning seems like a perfect tool for enabling novel medical imaging applications by tackling some of its unique challenges such as large and high-dimensional datasets with unorthodox structure, extremely fine signals, and massive diversity with limited data. The reality isn't that simple, and the commonly used tools greatly limit what we are capable of doing. In this talk, we will discuss some of the unique challenges to medical deep learning, what we can do about it, and how those things can result in much better models in practice.
Diogo Moitinho de Almeida is a data scientist, software engineer, and hacker. He has previously been a medalist at the International Math Olympiad ending a 13-year losing streak for the Philippines, received the top prize in the Interdisciplinary Contest in Modeling achieving the highest distinction of any team from the Western Hemisphere, and won a Kaggle competition setting a new state-of-the-art for black box identification of causality and getting the opportunity to speak at the Conference on Neural Information Processing Systems. As a lifelong learner and big fan of online education, he has taken more classes online than he has getting his undergraduate degree at the Rensselaer Polytechnic Institute. He loves all things software and enjoys contributing to open source, giving talks on things that he's built, and improving his Emacs setup.