Invited Talk – Raghavendra Selvan (Univ. Copenhagen) – Quantum Tensor Networks for Medical Image Analysis, May 26th 2021, 10-12h CET

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It’s a great pleasure to welcome Raghav Selvan from the University of Copenhagen at our lab!

Title: Quantum Tensor Networks for Medical Image Analysis
Date: May 26th 2021, 10-12h CET

Abstract: Quantum Tensor Networks (QTNs) provide efficient approximations of operations involving high dimensional tensors and have been extensively used in modelling quantum many-body systems and also to compress large neural networks. More recently, supervised learning has been attempted with tensor networks, and have primarily focused on classification of 1D signals and small images. In this talk, we will look at two formulations of QTN-based models for 2D & 3D medical image classification and 2D  medical image segmentation. Both the classification and segmentation models use the matrix product state (MPS) tensor network under the hood, which efficiently learns linear decision rules in high dimensional spaces. These QTN models are fully linear, end-to-end trainable using backpropagation and have lower GPU memory footprint than convolutional neural networks (CNN). We show competitive performance compared to relevant CNN baselines on multiple datasets for classification and segmentation tasks while presenting interesting connections to other existing supervised learning methods.

Bio: Raghavendra Selvan (Raghav) is currently an Assistant Professor at the University of Copenhagen, with joint responsibilities at the Machine Learning Section (Dept. of Computer Science), Kiehn Lab (Department of Neuroscience) and the Data Science Laboratory. He received his PhD in Medical Image Analysis (University of Copenhagen, 2018), his MSc degree in Communication Engineering in 2015 (Chalmers University, Sweden) and his Bachelor degree in Electronics and Communication Engineering degree in 2009 (BMS Institute of Technology, India). Raghavendra Selvan was born in Bangalore, India.

His current research interests are broadly pertaining Medical Image Analysis using Quantum Tensor Networks, Resource efficient ML, Bayesian Machine Learning, Graph-neural networks, Approximate Inference and multi-object tracking theory.