Zobrazeno 1 - 10
of 180
pro vyhledávání: '"PHAN, RAPHAËL"'
To facilitate early detection of breast cancer, there is a need to develop short-term risk prediction schemes that can prescribe personalized/individualized screening mammography regimens for women. In this study, we propose a new deep learning archi
Externí odkaz:
http://arxiv.org/abs/2412.03081
Brain tumor detection in multiplane Magnetic Resonance Imaging (MRI) slices is a challenging task due to the various appearances and relationships in the structure of the multiplane images. In this paper, we propose a new You Only Look Once (YOLO)-ba
Externí odkaz:
http://arxiv.org/abs/2410.21822
Autor:
Loo, Junn Yong, Adeline, Michelle, Pal, Arghya, Baskaran, Vishnu Monn, Ting, Chee-Ming, Phan, Raphael C. -W.
Energy based models (EBMs) are appealing for their generality and simplicity in data likelihood modeling, but have conventionally been difficult to train due to the unstable and time-consuming implicit MCMC sampling during contrastive divergence trai
Externí odkaz:
http://arxiv.org/abs/2407.15238
This paper introduces a novel convolutional neural networks (CNN) framework tailored for end-to-end audio deep learning models, presenting advancements in efficiency and explainability. By benchmarking experiments on three standard speech emotion rec
Externí odkaz:
http://arxiv.org/abs/2405.01815
Existing brain tumor segmentation methods usually utilize multiple Magnetic Resonance Imaging (MRI) modalities in brain tumor images for segmentation, which can achieve better segmentation performance. However, in clinical applications, some modaliti
Externí odkaz:
http://arxiv.org/abs/2404.14019
Autor:
Dass, Sharana Dharshikgan Suresh, Barua, Hrishav Bakul, Krishnasamy, Ganesh, Paramesran, Raveendran, Phan, Raphael C. -W.
Human action or activity recognition in videos is a fundamental task in computer vision with applications in surveillance and monitoring, self-driving cars, sports analytics, human-robot interaction and many more. Traditional supervised methods requi
Externí odkaz:
http://arxiv.org/abs/2404.06243
The low-rank plus sparse (L+S) decomposition model has enabled better reconstruction of dynamic magnetic resonance imaging (dMRI) with separation into background (L) and dynamic (S) component. However, use of low-rank prior alone may not fully explai
Externí odkaz:
http://arxiv.org/abs/2401.16928
Publikováno v:
In ICIP (2024) 2970--2974
Medical image semantic segmentation techniques can help identify tumors automatically from computed tomography (CT) scans. In this paper, we propose a Contextual and Attentional feature Fusions enhanced Convolutional Neural Network (CNN) and Transfor
Externí odkaz:
http://arxiv.org/abs/2401.16886
Publikováno v:
Image Vis. Comput. 147 (2024) 105057
We propose a novel Attentional Scale Sequence Fusion based You Only Look Once (YOLO) framework (ASF-YOLO) which combines spatial and scale features for accurate and fast cell instance segmentation. Built on the YOLO segmentation framework, we employ
Externí odkaz:
http://arxiv.org/abs/2312.06458