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of 179
pro vyhledávání: '"Dam, Erik B"'
The recent advances in deep learning (DL) have been accelerated by access to large-scale data and compute. These large-scale resources have been used to train progressively larger models which are resource intensive in terms of compute, data, energy,
Externí odkaz:
http://arxiv.org/abs/2403.12562
Autor:
Friis-Jensen, Ulrik, Johansen, Frederik L., Anker, Andy S., Dam, Erik B., Jensen, Kirsten M. Ø., Selvan, Raghavendra
Publikováno v:
KDD '24: Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 2024
Advances in graph machine learning (ML) have been driven by applications in chemistry as graphs have remained the most expressive representations of molecules. While early graph ML methods focused primarily on small organic molecules, recently, the s
Externí odkaz:
http://arxiv.org/abs/2402.13221
Automated segmentation of distinct tumor regions is critical for accurate diagnosis and treatment planning in pediatric brain tumors. This study evaluates the efficacy of the Multi-Planner U-Net (MPUnet) approach in segmenting different tumor subregi
Externí odkaz:
http://arxiv.org/abs/2401.06499
This paper introduces a comprehensive approach for segmenting regions of interest (ROI) in diverse medical imaging datasets, encompassing ultrasound, CT scans, and X-ray images. The proposed method harnesses the capabilities of the YOLOv8 model for a
Externí odkaz:
http://arxiv.org/abs/2310.12995
Publikováno v:
Lecture Notes Comp. Sci.14394 (2023)
The accelerated development of machine learning methods, primarily deep learning, are causal to the recent breakthroughs in medical image analysis and computer aided intervention. The resource consumption of deep learning models in terms of amount of
Externí odkaz:
http://arxiv.org/abs/2303.10181
Autor:
Selvan, Raghavendra, Bhagwat, Nikhil, Anthony, Lasse F. Wolff, Kanding, Benjamin, Dam, Erik B.
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 footprint of developing DL models for medical image analysis (MIA), w
Externí odkaz:
http://arxiv.org/abs/2203.02202
Publikováno v:
Journal of Machine Learning for Biomedical Imaging. 2022:005. pp 1-24
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 many-body systems and more recently are witnessing increased applicatio
Externí odkaz:
http://arxiv.org/abs/2109.07138
Tensor networks provide an efficient approximation of operations involving high dimensional tensors and have been extensively used in modelling quantum many-body systems. More recently, supervised learning has been attempted with tensor networks, pri
Externí odkaz:
http://arxiv.org/abs/2102.06900
The recently introduced locally orderless tensor network (LoTeNet) for supervised image classification uses matrix product state (MPS) operations on grids of transformed image patches. The resulting patch representations are combined back together in
Externí odkaz:
http://arxiv.org/abs/2011.06982
Publikováno v:
Journal of Machine Learning for Biomedical Imaging. 2021:5. pp 1-21. Special Issue: Medical Imaging with Deep Learning (MIDL) 2020
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 seen a recent surge of interest in relation to supervised learning ta
Externí odkaz:
http://arxiv.org/abs/2009.12280