Deep multi-path network integrating incomplete biomarker and chest CT data for evaluating lung cancer risk
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Yuankai Huo | Bennett A. Landman | Yucheng Tang | Riqiang Gao | Pierre P. Massion | Kaiwen Xu | Kim L. Sandler | Sanja L. Antic | Michael N. Kammer | Steve Deppen
[1] Peter Bühlmann,et al. MissForest - non-parametric missing value imputation for mixed-type data , 2011, Bioinform..
[2] Yuankai Huo,et al. Internal-transfer Weighting of Multi-task Learning for Lung Cancer Detection , 2020, Medical Imaging: Image Processing.
[3] S. Swensen,et al. The probability of malignancy in solitary pulmonary nodules. Application to small radiologically indeterminate nodules. , 1997, Archives of internal medicine.
[4] Bo Jiang,et al. MisGAN: Learning from Incomplete Data with Generative Adversarial Networks , 2019, ICLR.
[5] Pablo M. Olmos,et al. Handling Incomplete Heterogeneous Data using VAEs , 2018, Pattern Recognit..
[6] Shunxing Bao,et al. Multi-path x-D recurrent neural networks for collaborative image classification , 2020, Neurocomputing.
[7] Lori Stewart,et al. Prediction of lung cancer risk at follow-up screening with low-dose CT: a training and validation study of a deep learning method. , 2019, The Lancet. Digital health.
[8] D. Lynch,et al. The National Lung Screening Trial: overview and study design. , 2011, Radiology.
[9] A. Jemal,et al. Cancer statistics, 2019 , 2019, CA: a cancer journal for clinicians.
[10] Stef van Buuren,et al. Flexible Imputation of Missing Data , 2012 .
[11] D. Rubin. INFERENCE AND MISSING DATA , 1975 .
[12] Shunxing Bao,et al. Time-distanced gates in long short-term memory networks , 2020, Medical Image Anal..
[13] Zhe Li,et al. Evaluate the Malignancy of Pulmonary Nodules Using the 3-D Deep Leaky Noisy-OR Network , 2017, IEEE Transactions on Neural Networks and Learning Systems.
[14] Constantine Frangakis,et al. Multiple imputation by chained equations: what is it and how does it work? , 2011, International journal of methods in psychiatric research.
[15] Mihaela van der Schaar,et al. GAIN: Missing Data Imputation using Generative Adversarial Nets , 2018, ICML.
[16] S. Cummings,et al. Estimating the probability of malignancy in solitary pulmonary nodules. A Bayesian approach. , 1986, The American review of respiratory disease.
[17] Max Welling,et al. Attention-based Deep Multiple Instance Learning , 2018, ICML.
[18] Yuankai Huo,et al. Deep Multi-task Prediction of Lung Cancer and Cancer-free Progression from Censored Heterogenous Clinical Imaging , 2020, Medical Imaging: Image Processing.
[19] Yan Shen,et al. Brain Tumor Segmentation on MRI with Missing Modalities , 2019, IPMI.
[20] P. Massion,et al. Compensated Interferometry Measures of CYFRA 21-1 Improve Diagnosis of Lung Cancer. , 2019, ACS combinatorial science.
[21] Shunxing Bao,et al. Distanced LSTM: Time-Distanced Gates in Long Short-Term Memory Models for Lung Cancer Detection , 2019, MLMI@MICCAI.
[22] C. Gatsonis,et al. Reduced Lung-Cancer Mortality with Low-Dose Computed Tomographic Screening , 2012 .