Episode 13: Convolutional neural networks

30 November, 2021

#podcast #T2E5

Continuing our series on neural networks, this time it is the turn of convolutional neural networks. Responsible for the great popularity of deep learning in recent years, convolutional networks have drastically changed how we design systems for images. In this episode we talk about convolutions, primary classes and COVID with our colleague Anabel Gómez Ríos.

Anabel Gómez has just published a paper on COVID-19 prediction from X-ray images, using convolutional networks. Anabel has a double degree in Computer Science and Mathematics, with a Master in Data Science and Computer Engineering. She is currently doing her PhD with a FPU fellowship on image preprocessing and analysis using deep learning at our institute, the Andalusian Institute of Artificial Intelligence DaSCI. 

Check the links of this episode and listen to it!

Listen “SintonIA 13 – Redes neuronales convolucionales” at Spreaker.

Links of interest

• A last-in first-out stack data structure implemented in DNA. Annunziata Lopiccolo, Ben Shirt-Ediss, Emanuela Torelli, Abimbola Feyisara Adedeji Olulana, Matteo Castronovo, Harold Fellermann & Natalio Krasnogor. NATURE COMMUNICATIONS (2021)12:4861. https://doi.org/10.1038/s41467-021-25023-6.
• The Klimt Color Enigma. https://www.youtube.com/watch?v=ZS4mEQCRz2Y.
• Proyecto VRAILEXIA. https://vrailexia.eu/es/.
• Becas FPU. https://www.universidades.gob.es/portal/site/universidades/menuitem.3fa82a7cab101038d5895bd0026041a0/?vgnextoid=05b2fb16410ec710VgnVCM1000001d04140aRCRD.
• Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans. Michael Roberts, Derek Driggs, Matthew Thorpe, Julian Gilbey , Michael Yeung, Stephan Ursprung, Angelica I. Aviles-Rivero, Christian Etmann, Cathal McCague, Lucian Beer, Jonathan R. Weir-McCall , Zhongzhao Teng, Effrossyni Gkrania-Klotsas, AIX-COVNET, James H. F. Rudd, Evis Sala and Carola-Bibiane Schönlieb. NATURE MACHINE INTELLIGENCE, VOL 3, MARCH 2021, 199–217. https://www.nature.com/articles/s42256-021-00307-0.pdf.