Although current research aims to use and improve deep learning networks by applying knowledge about the structure and function of the healthy human brain and vice versa, the potential of using such networks to model neurodegenerative diseases remains largely understudied. In this work, we present a novel feasibility study modeling dementia in silico with deep convolutional neural networks. Therefore, deep convolutional neural networks were fully trained to perform visual object recognition, and then progressively injured in two distinct ways. More precisely, damage was progressively inflicted mimicking neuronal as well as synaptic injury. Synaptic injury was applied by randomly deleting weights in the network, while neuronal injury was simulated by removing full nodes or filters in the network. After each iteration of injury, network object recognition accuracy was evaluated. Saliency maps were generated using the uninjured and injured networks and quantitatively compared using the structural similarity index measure for test set images to further investigate the loss of visual cognition. The quantitative evaluation revealed cognitive function of the network progressively decreased with increasing injury load. This effect was more pronounced for synaptic damage. As damage increased, the model focus shifted away from the main objects in the images and became more dispersed. This shift in attention was quantitatively evidenced by a decrease in the structural similarity index measure comparing the saliency maps of corresponding uninjured and injured models, as a function of injury. The results of this study provide a promising foundation to develop in silico models of neurodegenerative diseases using deep learning networks. The effects of neurodegeneration found for the in silico model are especially similar to the loss of visual cognition seen in patients with posterior cortical atrophy.
Jasmine Moore (University of Calgary)
Anup Tuladhar (University of Calgary)
My focus is on machine learning solutions for medicine, with specific focus on convolutional neural networks and distributed learning. I come from an alternative background: My PhD thesis was on a biomaterials-based drug delivery system for local drug release in the stroke-injured brain.
Nils Daniel Forkert (University of Calgary)
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2019 : Poster Session I »
Shuangjia Zheng · Arnav Kapur · Umar Asif · Eyal Rozenberg · Cyprien Gilet · Oleksii Sidorov · Yogesh Kumar · Tom Van Steenkiste · William Boag · David Ouyang · Paul Jaeger · Sheng Liu · Aparna Balagopalan · Deepta Rajan · Marta Skreta · Nikhil Pattisapu · Jann Goschenhofer · Viraj Prabhu · Di Jin · Laura-Jayne Gardiner · Irene Li · sriram kumar · Qiyuan Hu · Mehul Motani · Justin Lovelace · Usman Roshan · Lucy Lu Wang · Ilya Valmianski · Hyeonwoo Lee · Sunil Mallya · Elias Chaibub Neto · Jonas Kemp · Marie Charpignon · Amber Nigam · Wei-Hung Weng · Sabri Boughorbel · Alexis Bellot · Lovedeep Gondara · Haoran Zhang · Mohammad Taha Bahadori · John Zech · Rulin Shao · Edward Choi · Laleh Seyyed-Kalantari · Emily Aiken · Ioana Bica · Yiqiu Shen · Kieran Chin-Cheong · Subhrajit Roy · Ioana Baldini · So Yeon Min · Dirk Deschrijver · Pekka Marttinen · Damian Pascual Ortiz · Supriya Nagesh · Niklas Rindtorff · Andriy Mulyar · Katharina Hoebel · Martha Shaka · Pierre Machart · Leon Gatys · Nathan Ng · Matthias Hüser · Devin Taylor · Dennis Barbour · Natalia Martinez · Clara McCreery · Benjamin Eyre · Vivek Natarajan · Ren Yi · Ruibin Ma · Chirag Nagpal · Nan Du · Chufan Gao · Anup Tuladhar · Sam Shleifer · Jason Ren · Pouria Mashouri · Ming Yang Lu · Farideh Bagherzadeh-Khiabani · Olivia Choudhury · Maithra Raghu · Scott Fleming · Mika Jain · GUO YANG · Alena Harley · Stephen Pfohl · Elisabeth Rumetshofer · Alex Fedorov · Saloni Dash · Jacob Pfau · Sabina Tomkins · Colin Targonski · Michael Brudno · Xinyu Li · Yiyang Yu · Nisarg Patel