A foundation generative model for breast ultrasound image analysis
Haojun Yu, Youcheng Li, Nan Zhang, Zihan Niu et autres
cn, Éthiopie, us (code pays fourni par la source)
Informations fournies par OpenAlex. Research Africa ne déduit ni nationalité, ni poste, ni coordonnées personnelles.
Haojun Yu, Youcheng Li, Nan Zhang, Zihan Niu et autres
cn, Éthiopie, us (code pays fourni par la source)
Jie Han, Yuanjing Gao, Ling Huo, Dong Wang et autres
BACKGROUND: The clinical application of artificial intelligence (AI) models based on breast ultrasound static images has been hindered in real-world workflows due to operator-dependence of standardized image acquisition and incomplete view of breast lesions on static images. To better exploit the real-time …
cn, us, Éthiopie (code pays fourni par la source)
Liwei Wang, Haojun Yu, You‐Cheng Li, Nan Zhang et autres
cn, Éthiopie, us, tw (code pays fourni par la source)
Haojun Yu, You‐Cheng Li, Nan Zhang, Zihan Niu et autres
Foundational models have emerged as powerful tools for addressing various tasks in clinical settings. However, their potential development to breast ultrasound analysis remains untapped. In this paper, we present BUSGen, the first foundational generative model specifically designed for breast ultrasound image analysis. …
Jigang Fan, Quanlin Wu, Shengjie Luo, Liwei Wang
The detection of ligand binding sites for proteins is a fundamental step in Structure-Based Drug Design. Despite notable advances in recent years, existing methods, datasets, and evaluation metrics are confronted with several key challenges: (1) current datasets and methods are centered on …
us, cn (code pays fourni par la source)
Haojun Yu, You‐Cheng Li, Nan Zhang, Zihan Niu et autres
Data-driven deep learning models have shown great capabilities to assist radiologists in breast ultrasound (US) diagnoses. However, their effectiveness is limited by the long-tail distribution of training data, which leads to inaccuracies in rare cases. In this study, we address a long-standing …
Quanlin Wu, Hang Ye, Yuntian Gu, Huishuai Zhang et autres
In this paper, we propose a new self-supervised method, which is called Denoising Masked AutoEncoders (DMAE), for learning certified robust classifiers of images. In DMAE, we corrupt each image by adding Gaussian noises to each pixel value and randomly masking several patches. …
Quanlin Wu, Hang Ye, Yuntian Gu
In this paper, we propose a novel guided diffusion purification approach to provide a strong defense against adversarial attacks. Our model achieves 89.62% robust accuracy under PGD-L_inf attack (eps = 8/255) on the CIFAR-10 dataset. We first explore the essential correlations between …
Xiaoliang Li, Quanlin Wu, Fu-Jun Ma, Xinxin Zhang et autres
Hepatocellular carcinoma (HCC) is the most common primary liver tumor and one of the leading causes of cancer-related death worldwide. Chemotherapeutic agents/regimens such as cisplatin (DDP) are frequently used for advanced HCC treatment. However, drug resistance remains a major hindrance and the …
us, cn (code pays fourni par la source)
Objective To explore the expression of human leukocyte antigen-DR (HLA-DR) in hepatocellular carcinoma and its clinical values in predicting prognosis. Methods The primary hepatocellular carcinoma tissues of 97 patients in our hospital from August 2012 to February 2014 were enrolled as subjects …
cn (code pays fourni par la source)
He Yaopeng, Jiao Wenping, Deng Rui, Li Xiaolong et autres
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