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Segmenting Medical Images with Limited Data

Autor: Liua, Zhaoshan, Lv, Qiujie, Lee, Chau Hung, Shen, Lei

While computer vision has proven valuable for medical image segmentation, its application faces challenges such as limited dataset sizes and the complexity of effectively leveraging unlabeled images. To address these challenges, we present a novel se

Externí odkaz: http://arxiv.org/abs/2407.09189

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Scalable Crystal Structure Relaxation Using an Iteration-Free Deep Generative Model with Uncertainty Quantification

Autor: Yang, Ziduo, Zhao, Yi-Ming, Wang, Xian, Liu, Xiaoqing, Zhang, Xiuying, Li, Yifan, Lv, Qiujie, Chen, Calvin Yu-Chian, Shen, Lei

In computational molecular and materials science, determining equilibrium structures is the crucial first step for accurate subsequent property calculations. However, the recent discovery of millions of new crystals and complex twisted structures has

Externí odkaz: http://arxiv.org/abs/2404.00865

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Lightweight equivariant model for efficient machine learning interatomic potentials

Autor: Yang, Ziduo, Wang, Xian, Li, Yifan, Lv, Qiujie, Chen, Calvin Yu-Chian, Shen, Lei

In modern computational materials science, deep learning has shown the capability to predict interatomic potentials, thereby supporting and accelerating conventional simulations. However, existing models typically sacrifice either accuracy or efficie

Externí odkaz: http://arxiv.org/abs/2311.02869

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MedAugment: Universal Automatic Data Augmentation Plug-in for Medical Image Analysis

Autor: Liu, Zhaoshan, Lv, Qiujie, Li, Yifan, Yang, Ziduo, Shen, Lei

Data augmentation (DA) has been widely leveraged in computer vision to alleviate the data shortage, whereas the DA in medical image analysis (MIA) faces multiple challenges. The prevalent DA approaches in MIA encompass conventional DA, synthetic DA,

Externí odkaz: http://arxiv.org/abs/2306.17466

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Recent Progress in Transformer-based Medical Image Analysis

Autor: Liu, Zhaoshan, Lv, Qiujie, Yang, Ziduo, Li, Yifan, Lee, Chau Hung, Shen, Lei

The transformer is primarily used in the field of natural language processing. Recently, it has been adopted and shows promise in the computer vision (CV) field. Medical image analysis (MIA), as a critical branch of CV, also greatly benefits from thi

Externí odkaz: http://arxiv.org/abs/2208.06643

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GSDA: Generative Adversarial Network-based Semi-Supervised Data Augmentation for Ultrasound Image Classification

Autor: Liu, Zhaoshan, Lv, Qiujie, Lee, Chau Hung, Shen, Lei

Medical Ultrasound (US) is one of the most widely used imaging modalities in clinical practice, but its usage presents unique challenges such as variable imaging quality. Deep Learning (DL) models can serve as advanced medical US image analysis tools

Externí odkaz: http://arxiv.org/abs/2203.06184

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