Results 161 to 170 of about 117,293 (275)
Abstract Brain tumour segmentation employing MRI images is important for disease diagnosis, monitoring, and treatment planning. Till now, many encoder‐decoder architectures have been developed for this purpose, with U‐Net being the most extensively utilised. However, these architectures require a lot of parameters to train and have a semantic gap. Some
Muhammad Zeeshan Aslam +3 more
wiley +1 more source
Enhancing single shot unsupervised domain adaptation for inter-camera person re-identification. [PDF]
Vidhyalakshmi MK +4 more
europepmc +1 more source
Abstract Abnormalities in the heart's rhythm, known as arrhythmias, pose a significant threat to global health, often leading to severe cardiac conditions and sudden cardiac deaths. Therefore, early and accurate detection of arrhythmias is crucial for timely intervention and potentially life‐saving treatment.
Hasnain Ali Shah +4 more
wiley +1 more source
Optimizing Radiographic Diagnosis Through Signal-Balanced Convolutional Models. [PDF]
Neemuchwala SJ +5 more
europepmc +1 more source
ABSTRACT Large language models (LLMs) have made remarkable advances in natural language processing, demonstrating great potential in modelling structured sequences. However, adapting these capabilities to machine gaming tasks such as Go remains challenging due to limitations in strategy generalisation and optimisation efficiency.
Xiali Li +5 more
wiley +1 more source
Urban road surface crack detection based on U-net and ResNeXt network. [PDF]
Qiao J, Wang H, Zhou Z, Meng Y, Gong M.
europepmc +1 more source
Enhancing Generalisation via Cascaded Inertia SGD With Learnt Hyperparameters
ABSTRACT A central challenge in deep learning lies in achieving strong model generalisation, an area in which conventional optimisers such as stochastic gradient descent (SGD) often exhibit limitations, even though they ensure convergence. This paper introduces cascaded inertia SGD (CISGD), a novel optimisation algorithm specifically designed to ...
Yongji Guan +3 more
wiley +1 more source
Evaluating deep learning models for pancreatic cancer diagnosis. [PDF]
Li D, He H, Hu J, Ding Y, Kong L, Hu A.
europepmc +1 more source
AT‐AER: Adversarial Training With Adaptive Example Reuse
ABSTRACT Adversarial training (AT) is widely regarded as a crucial defense method for deep neural networks against adversarial attacks. Most of the existing AT methods suffer from the problems of insufficient coverage of perturbation space and robust overfitting.
Meng Hu +5 more
wiley +1 more source
A Quantised Push‐Sum Distributed Adaptive Momentum Algorithm for Optimisation Over Directed Networks
ABSTRACT In this paper, we investigate a distributed constrained optimisation problem over directed networks. The agents in the networks conduct local computations and communications, endeavouring to collaboratively minimise the aggregation of all locally known convex cost functions subject to a global constraint set.
Qingguo Lü +6 more
wiley +1 more source

