68 citations · 184 across the 11 of their papers we have counts for
11 papers
UNet Architectures in Multiplanar Volumetric Segmentation -- Validated on Three Knee MRI Cohorts
Sandeep Singh Sengara, Christopher Meulengrachtb, Mikael Ploug Boesenb +4
UNet has become the gold standard method for segmenting 2D medical images that any new method must be validated against. However, in recent years, several variations of the seminal…
Carbon Footprint of Selecting and Training Deep Learning Models for Medical Image Analysis
Raghavendra Selvan, Nikhil Bhagwat, Lasse F. Wolff Anthony +2
The increasing energy consumption and carbon footprint of deep learning (DL) due to growing compute requirements has become a cause of concern. In this work, we focus on the carbon…
Patch-based Medical Image Segmentation using Matrix Product State Tensor Networks
Raghavendra Selvan, Erik B Dam, Søren Alexander Flensborg +1
Tensor networks are efficient factorisations of high-dimensional tensors into a network of lower-order tensors. They have been most commonly used to model entanglement in quantum m…
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…