Zobrazeno 1 - 10
of 275
pro vyhledávání: '"Zhang, Yejia"'
Masked Autoencoders (MAEs) have been shown to be effective in pre-training Vision Transformers (ViTs) for natural and medical image analysis problems. By reconstructing missing pixel/voxel information in visible patches, a ViT encoder can aggregate c
Externí odkaz:
http://arxiv.org/abs/2406.10519
Autor:
Sapkota, Nishchal, Zhang, Yejia, Li, Sirui, Liang, Peixian, Zhao, Zhuo, Zhang, Jingjing, Zha, Xiaomin, Zhou, Yiru, Cao, Yunxia, Chen, Danny Z
Male infertility accounts for about one-third of global infertility cases. Manual assessment of sperm abnormalities through head morphology analysis encounters issues of observer variability and diagnostic discrepancies among experts. Its alternative
Externí odkaz:
http://arxiv.org/abs/2402.03697
Autor:
Sapkota, Nishchal, Zhang, Yejia, Perrine, Susan M. Motch, Hsi, Yuhan, Li, Sirui, Wu, Meng, Holmes, Greg, Abdulai, Abdul R., Jabs, Ethylin W., Richtsmeier, Joan T., Chen, Danny Z
Studying the morphological development of cartilaginous and osseous structures is critical to the early detection of life-threatening skeletal dysmorphology. Embryonic cartilage undergoes rapid structural changes within hours, introducing biological
Externí odkaz:
http://arxiv.org/abs/2402.03695
Conformal prediction (CP) generates a set of predictions for a given test sample such that the prediction set almost always contains the true label (e.g., 99.5\% of the time). CP provides comprehensive predictions on possible labels of a given test s
Externí odkaz:
http://arxiv.org/abs/2309.04760
Modern medical image segmentation methods primarily use discrete representations in the form of rasterized masks to learn features and generate predictions. Although effective, this paradigm is spatially inflexible, scales poorly to higher-resolution
Externí odkaz:
http://arxiv.org/abs/2307.12429
Understanding of spatial attributes is central to effective 3D radiology image analysis where crop-based learning is the de facto standard. Given an image patch, its core spatial properties (e.g., position & orientation) provide helpful priors on exp
Externí odkaz:
http://arxiv.org/abs/2211.08643
Convolutional neural network (CNN) based methods have achieved great successes in medical image segmentation, but their capability to learn global representations is still limited due to using small effective receptive fields of convolution operation
Externí odkaz:
http://arxiv.org/abs/2211.08564
Self-supervised instance discrimination is an effective contrastive pretext task to learn feature representations and address limited medical image annotations. The idea is to make features of transformed versions of the same images similar while for
Externí odkaz:
http://arxiv.org/abs/2211.08557
High annotation costs and limited labels for dense 3D medical imaging tasks have recently motivated an assortment of 3D self-supervised pretraining methods that improve transfer learning performance. However, these methods commonly lack spatial aware
Externí odkaz:
http://arxiv.org/abs/2211.08533
With rising male infertility, sperm head morphology classification becomes critical for accurate and timely clinical diagnosis. Recent deep learning (DL) morphology analysis methods achieve promising benchmark results, but leave performance and robus
Externí odkaz:
http://arxiv.org/abs/2202.07191