All Outputs (2)

Two layer ensemble of deep learning models for medical image segmentation. [Preprint] (2021)
Working Paper
DANG, T., NGUYEN, T.T., MCCALL, J., ELYAN, E. and MORENO-GARCÍA, C.F. 2021. Two layer ensemble of deep learning models for medical image segmentation. arXiv [online]. Available from: https://doi.org/10.48550/arXiv.2104.04809

In recent years, deep learning has rapidly become a method of choice for the segmentation of medical images. Deep Neural Network (DNN) architectures such as UNet have achieved state-of-the-art results on many medical datasets. To further improve the... Read More about Two layer ensemble of deep learning models for medical image segmentation. [Preprint].

Traditional and contemporary approaches to mathematical fitness-fatigue models in exercise science: a practical guide with resources. Part II. (2021)
Working Paper
SWINTON, P., STEPHENS HEMINGWAY, B., RASCHE, C., PFEIFFER, M. and OGOREK, B. 2021. Traditional and contemporary approaches to mathematical fitness-fatigue models in exercise science: a practical guide with resources. Part II. SportRxiv [online]. Available from: https://doi.org/10.31236/osf.io/5qgc2

The standard fitness-fatigue model (FFM) is known to include several limitations described by the linearity assumption, the independence assumption and the deterministic assumption. These limitations ensure that the modelled response to chronic train... Read More about Traditional and contemporary approaches to mathematical fitness-fatigue models in exercise science: a practical guide with resources. Part II..