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Found 6 papers, 4 papers with code
Equity through Access: A Case for Small-scale Deep Learning
The recent advances in deep learning (DL) have been accelerated by access to large-scale data and compute.
Fully Automated Tumor Segmentation for Brain MRI data using Multiplanner UNet
Automated segmentation of distinct tumor regions is critical for accurate diagnosis and treatment planning in pediatric brain tumors.
Operating critical machine learning models in resource constrained regimes
The resource consumption of deep learning models in terms of amount of training data, compute and energy costs are known to be massive.
Patch-based Medical Image Segmentation using Matrix Product State Tensor Networks
The performance of the proposed tensor network segmentation model is compared with relevant baseline methods.
Segmenting two-dimensional structures with strided tensor networks
We use the matrix product state (MPS) tensor network on non-overlapping patches of a given input image to predict the segmentation mask by learning a pixel-wise linear classification rule in a high dimensional space.
Multi-layered tensor networks for image classification
The recently introduced locally orderless tensor network (LoTeNet) for supervised image classification uses matrix product state (MPS) operations on grids of transformed image patches.
