GaitSet: Cross-View Gait Recognition Through Utilizing Gait As a Deep Set von Chao, Hanqing, Wang, Kun, He, Yiwei, Zhang, Junping, Feng, Jianfeng

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Deep learning predicts cardiovascular disease risks from lung cancer screening low dose computed tomography von Chao, Hanqing, Shan, Hongming, Homayounieh, Fatemeh, Singh, Ramandeep, Khera, Ruhani Doda, Guo, Hengtao, Su, Timothy, Wang, Ge, Kalra, Mannudeep K., Yan, Pingkun

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Integrative analysis for COVID-19 patient outcome prediction von Chao, Hanqing, Fang, Xi, Zhang, Jiajin, Homayounieh, Fatemeh, Arru, Chiara D., Digumarthy, Subba R., Babaei, Rosa, Mobin, Hadi K., Mohseni, Iman, Saba, Luca, Carriero, Alessandro, Falaschi, Zeno, Pasche, Alessio, Wang, Ge, Kalra, Mannudeep K., Yan, Pingkun

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Unbiasing Fairness Evaluation of Radiology AI Model von Liang, Yuxuan, Chao, Hanqing, Zhang, Jiajin, Wang, Ge, Yan, Pingkun

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Association of AI quantified COVID-19 chest CT and patient outcome von Fang, Xi, Kruger, Uwe, Homayounieh, Fatemeh, Chao, Hanqing, Zhang, Jiajin, Digumarthy, Subba R., Arru, Chiara D., Kalra, Mannudeep K., Yan, Pingkun

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Multimodal radiology AI von Yan, Pingkun, Wang, Ge, Chao, Hanqing, Kalra, Mannudeep K.

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Towards Adversarial Robustness in Unlabeled Target Domains von Zhang, Jiajin, Chao, Hanqing, Yan, Pingkun

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Toward Adversarial Robustness in Unlabeled Target Domains von Zhang, Jiajin, Chao, Hanqing, Yan, Pingkun

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Cross-modal attention for multi-modal image registration von Song, Xinrui, Chao, Hanqing, Xu, Xuanang, Guo, Hengtao, Xu, Sheng, Turkbey, Baris, Wood, Bradford J., Sanford, Thomas, Wang, Ge, Yan, Pingkun

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Revisiting the Trustworthiness of Saliency Methods in Radiology AI von Zhang, Jiajin, Chao, Hanqing, Dasegowda, Giridhar, Wang, Ge, Kalra, Mannudeep K, Yan, Pingkun

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Ultrasound Frame-to-Volume Registration via Deep Learning for Interventional Guidance von Guo, Hengtao, Xu, Xuanang, Song, Xinrui, Xu, Sheng, Chao, Hanqing, Myers, Joshua, Turkbey, Baris, Pinto, Peter A., Wood, Bradford J., Yan, Pingkun

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Robust Precipitation Bias Correction Through an Ordinal Distribution Autoencoder von Luo, Youcheng, Xu, Xiaoyang, Liu, Yiqun, Chao, Hanqing, Chu, Hai, Chen, Lei, Zhang, Junping, Ma, Leiming, Wang, James Z.

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Identifying Progression-Specific Alzheimer's Subtypes Using Multimodal Transformer von Machado Reyes, Diego, Chao, Hanqing, Hahn, Juergen, Shen, Li, Yan, Pingkun, For The Alzheimer's Disease Neuroimaging Initiative

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Cross-Modal Attention for MRI and Ultrasound Volume Registration von Song, Xinrui, Guo, Hengtao, Xu, Xuanang, Chao, Hanqing, Xu, Sheng, Turkbey, Baris, Wood, Bradford J., Wang, Ge, Yan, Pingkun

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Regression Metric Loss: Learning a Semantic Representation Space for Medical Images von Chao, Hanqing, Zhang, Jiajin, Yan, Pingkun

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Deep learning-based motion artifact removal in functional near-infrared spectroscopy von Gao, Yuanyuan, Chao, Hanqing, Cavuoto, Lora, Yan, Pingkun, Kruger, Uwe, Norfleet, Jack E., Makled, Basiel A., Schwaitzberg, Steven, De, Suvranu, Intes, Xavier

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Deep Learning for Wireless Physical Layer： Opportunities and Challenges von Wang, Tianqi, Wen, Chao-Kai, Wang, Hanqing, Gao, Feifei, Jiang, Tao, Jin, Shi

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AnaXNet: Anatomy Aware Multi-label Finding Classification in Chest X-Ray von Agu, Nkechinyere N., Wu, Joy T., Chao, Hanqing, Lourentzou, Ismini, Sharma, Arjun, Moradi, Mehdi, Yan, Pingkun, Hendler, James

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Overlooked Trustworthiness of Saliency Maps von Zhang, Jiajin, Chao, Hanqing, Dasegowda, Giridhar, Wang, Ge, Kalra, Mannudeep K., Yan, Pingkun

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Task-Oriented Low-Dose CT Image Denoising von Zhang, Jiajin, Chao, Hanqing, Xu, Xuanang, Niu, Chuang, Wang, Ge, Yan, Pingkun

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