Lung Structures Enhancement in Chest Radiographs via CT Based FCNN Training
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[1] Rebecca L. Siegel Mph,et al. Cancer statistics, 2018 , 2018 .
[2] Lorenzo Bonomo,et al. Missed lung cancer: when, where, and why? , 2017, Diagnostic and interventional radiology.
[3] Kunio Doi,et al. Image-processing technique for suppressing ribs in chest radiographs by means of massive training artificial neural network (MTANN) , 2006, IEEE Transactions on Medical Imaging.
[4] Bram van Ginneken,et al. Segmentation of anatomical structures in chest radiographs using supervised methods: a comparative study on a public database , 2006, Medical Image Anal..
[5] Ronald M. Summers,et al. ChestX-ray: Hospital-Scale Chest X-ray Database and Benchmarks on Weakly Supervised Classification and Localization of Common Thorax Diseases , 2019, Deep Learning and Convolutional Neural Networks for Medical Imaging and Clinical Informatics.
[6] Thomas Brox,et al. U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.
[7] Raúl San José Estépar,et al. Emphysema quantification on simulated X-rays through deep learning techniques , 2018, 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018).
[8] A. Jemal,et al. Cancer statistics, 2018 , 2018, CA: a cancer journal for clinicians.
[9] Andrew Y. Ng,et al. CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning , 2017, ArXiv.
[10] Nassir Navab,et al. X-Ray In-Depth Decomposition: Revealing the Latent Structures , 2016, MICCAI.