QLunch: Raghavendra Selvan

Speaker: Raghavendra Selvan, Department of Computer Science, University of Copenhagen

Title: Quantum Tensor Networks for Medical Image Analysis

Abstract: Quantum Tensor Networks (QTNs) provide efficient approximations of operations involving high dimensional tensors and have been primarily used in modelling quantum many-body systems and also seen applications in neural network compression. More recently, supervised machine 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-inspired 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-inspired models are 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.