Zobrazeno 1 - 8
of 8
pro vyhledávání: '"Kalkhof, John"'
Continual learning (CL) in medical imaging presents a unique challenge, requiring models to adapt to new domains while retaining previously acquired knowledge. We introduce NCAdapt, a Neural Cellular Automata (NCA) based method designed to address th
Externí odkaz:
http://arxiv.org/abs/2410.23368
Medical image registration is a critical process that aligns various patient scans, facilitating tasks like diagnosis, surgical planning, and tracking. Traditional optimization based methods are slow, prompting the use of Deep Learning (DL) technique
Externí odkaz:
http://arxiv.org/abs/2410.22265
The disparity in access to machine learning tools for medical imaging across different regions significantly limits the potential for universal healthcare innovation, particularly in remote areas. Our research addresses this issue by implementing Neu
Externí odkaz:
http://arxiv.org/abs/2407.18114
Despite considerable success, large Denoising Diffusion Models (DDMs) with UNet backbone pose practical challenges, particularly on limited hardware and in processing gigapixel images. To address these limitations, we introduce two Neural Cellular Au
Externí odkaz:
http://arxiv.org/abs/2401.06291
Autor:
Kalkhof, John, Mukhopadhyay, Anirban
Medical image segmentation relies heavily on large-scale deep learning models, such as UNet-based architectures. However, the real-world utility of such models is limited by their high computational requirements, which makes them impractical for reso
Externí odkaz:
http://arxiv.org/abs/2309.02954
Access to the proper infrastructure is critical when performing medical image segmentation with Deep Learning. This requirement makes it difficult to run state-of-the-art segmentation models in resource-constrained scenarios like primary care facilit
Externí odkaz:
http://arxiv.org/abs/2302.03473
While machine learning approaches perform well on their training domain, they generally tend to fail in a real-world application. In cardiovascular magnetic resonance imaging (CMR), respiratory motion represents a major challenge in terms of acquisit
Externí odkaz:
http://arxiv.org/abs/2209.09678
Limited amount of labelled training data are a common problem in medical imaging. This makes it difficult to train a well-generalised model and therefore often leads to failure in unknown domains. Hippocampus segmentation from magnetic resonance imag
Externí odkaz:
http://arxiv.org/abs/2201.05650