116 citations · 174 across the 10 of their papers we have counts for
9 papers · 1 filter
Segmenting two-dimensional structures with strided tensor networks
Raghavendra Selvan, Erik B Dam, Jens Petersen
Tensor networks provide an efficient approximation of operations involving high dimensional tensors and have been extensively used in modelling quantum many-body systems. More rece…
Multi-layered tensor networks for image classification
Raghavendra Selvan, Silas Ørting, Erik B Dam
The recently introduced locally orderless tensor network (LoTeNet) for supervised image classification uses matrix product state (MPS) operations on grids of transformed image patc…
Locally orderless tensor networks for classifying two- and three-dimensional medical images
Raghavendra Selvan, Silas Ørting, Erik B Dam
Tensor networks are factorisations of high rank tensors into networks of lower rank tensors and have primarily been used to analyse quantum many-body problems. Tensor networks have…
Segmentation of Roots in Soil with U-Net
Abraham George Smith, Jens Petersen, Raghavendra Selvan +1
Plant root research can provide a way to attain stress-tolerant crops that produce greater yield in a diverse array of conditions. Phenotyping roots in soil is often challenging du…
Graph Refinement based Airway Extraction using Mean-Field Networks and Graph Neural Networks
Raghavendra Selvan, Thomas Kipf, Max Welling +4
Graph refinement, or the task of obtaining subgraphs of interest from over-complete graphs, can have many varied applications. In this work, we extract trees or collection of sub-t…
Extracting Tree-structures in CT data by Tracking Multiple Statistically Ranked Hypotheses
Raghavendra Selvan, Jens Petersen, Jesper H Pedersen +1
In this work, we adapt a method based on multiple hypothesis tracking (MHT) that has been shown to give state-of-the-art vessel segmentation results in interactive settings, for th…