Zobrazeno 1 - 10
of 74
pro vyhledávání: '"Lyu, Junyan"'
Photoacoustic Microscopy (PAM) images integrating the advantages of optical contrast and acoustic resolution have been widely used in brain studies. However, there exists a trade-off between scanning speed and image resolution. Compared with traditio
Externí odkaz:
http://arxiv.org/abs/2312.07226
Contrastive learning based vision-language joint pre-training has emerged as a successful representation learning strategy. In this paper, we present a prototype representation learning framework incorporating both global and local alignment between
Externí odkaz:
http://arxiv.org/abs/2307.12577
Autor:
Liu, Linfeng, Lyu, Junyan, Liu, Siyu, Tang, Xiaoying, Chandra, Shekhar S., Nasrallah, Fatima A.
The prediction of mild cognitive impairment (MCI) conversion to Alzheimer's disease (AD) is important for early treatment to prevent or slow the progression of AD. To accurately predict the MCI conversion to stable MCI or progressive MCI, we propose
Externí odkaz:
http://arxiv.org/abs/2307.07177
Self-supervised Learning (SSL) has been widely applied to learn image representations through exploiting unlabeled images. However, it has not been fully explored in the medical image analysis field. In this work, Saliency-guided Self-Supervised imag
Externí odkaz:
http://arxiv.org/abs/2210.10969
Convolutional neural networks have been widely applied to medical image segmentation and have achieved considerable performance. However, the performance may be significantly affected by the domain gap between training data (source domain) and testin
Externí odkaz:
http://arxiv.org/abs/2207.13249
Although deep learning based diabetic retinopathy (DR) classification methods typically benefit from well-designed architectures of convolutional neural networks, the training setting also has a non-negligible impact on the prediction performance. Th
Externí odkaz:
http://arxiv.org/abs/2110.14160
Publikováno v:
In Pattern Recognition October 2024 154
We innovatively propose a flexible and consistent face alignment framework, LDDMM-Face, the key contribution of which is a deformation layer that naturally embeds facial geometry in a diffeomorphic way. Instead of predicting facial landmarks via heat
Externí odkaz:
http://arxiv.org/abs/2108.00690
Manually annotating medical images is extremely expensive, especially for large-scale datasets. Self-supervised contrastive learning has been explored to learn feature representations from unlabeled images. However, unlike natural images, the applica
Externí odkaz:
http://arxiv.org/abs/2107.08274
Autor:
Lin, Li, Wang, Zhonghua, Wu, Jiewei, Huang, Yijin, Lyu, Junyan, Cheng, Pujin, Wu, Jiong, Tang, Xiaoying
Optical coherence tomography angiography (OCTA) is a novel non-invasive imaging technique that allows visualizations of vasculature and foveal avascular zone (FAZ) across retinal layers. Clinical researches suggest that the morphology and contour irr
Externí odkaz:
http://arxiv.org/abs/2107.04823