Skip to content

Latest commit

 

History

History
33 lines (22 loc) · 2.45 KB

abstract.md

File metadata and controls

33 lines (22 loc) · 2.45 KB

Deep Learning for the Health and the Life Sciences

Machine Learning (ML) continues to be increasingly important to biological and medical research. Across these diverse fields, advances in dataset size and availability, ML algorithm competence and computational power are transforming modern science. Deep Learning (DL) has been instrumental to this progress, bringing the promise to radically transform human wellness and healthcare. One of the striking advantages of DL over classical ML is its natural ability to integrate heterogeneous datasets, as well as multiple sources of information, and resolve arbitrarily complex relationships.

In this workshop, some of the most recent state-of-the-art solutions of DL for Biomedicine and Computational Biology will be presented. The tutorial is organised in three main parts, covering specific areas of the Health and the Life Sciences. In each part, multiple case studies will be examined. First, the data case will be introduced, along with the corresponding clinical/biological settings. Then, the proposed DL solution will be presented from a very technical perspective: from data preparation to the DL pipeline. To do so, the PyTorch Deep Learning framework will be used, along with the fully fledged Python data science ecosystem (e.g. pandas, numpy, sklearn).

The tutorial is intended for researchers interested in exploring the latest ML/DL solutions for the Health and the Life Sciences; and for practitioners who wants to learn more about the PyTorch framework.

Proficiency with the main structures of the Python language is required to attend the tutorial. Knowledge of the basics of statistical learning and computational biology is ideal, but not compulsory to attend the tutorial.

For a general introductory overview, it is highly recommended to also attend the workshop Introduction to Deep Learning.

At a glance, the tutorial will be structured in the following parts:

  1. Intro: Overview of main ML/DL concepts

    • ML flavours & DL frameworks
    • Introduction to the PyTorch framework
  2. Genetics & Genomics

    • Cell classification from a cell-line
    • Transcription Factor Binding
  3. Medical and BioImages

    • Diabetic Retinopathy from fundus images
    • Retina U-Net for Blood Vessel Segmentation
    • Transfer Learning and HIstopathological Images for Breast Cancer
  4. Mental Health and Wellbeing

  5. Unraveling the Black boxes

    • Hidden Layers and Embeddings
    • Few Notes on Model Interpretability