A novel computer‐aided lung nodule detection system for CT images M Tan, R Deklerck, B Jansen, M Bister, J Cornelis Medical physics 38 (10), 5630-5645, 2011 | 284 | 2011 |
Fusion of Quantitative Image and Genomic Biomarkers to Improve Prognosis Assessment of Early Stage Lung Cancer Patients N Emaminejad, W Qian, Y Guan, M Tan, Y Qiu, H Liu, B Zheng IEEE Transactions on Biomedical Engineering, 2015 | 129 | 2015 |
Optimization of breast mass classification using sequential forward floating selection (SFFS) and a support vector machine (SVM) model M Tan, J Pu, B Zheng International journal of computer assisted radiology and surgery 9, 1005-1020, 2014 | 95 | 2014 |
An initial investigation on developing a new method to predict short-term breast cancer risk based on deep learning technology Y Qiu, Y Wang, S Yan, M Tan, S Cheng, H Liu, B Zheng Medical Imaging 2016: Computer-Aided Diagnosis 9785, 517-522, 2016 | 94 | 2016 |
Lung nodule classification using deep local–global networks M Al-Shabi, BL Lan, WY Chan, KH Ng, M Tan International journal of computer assisted radiology and surgery 14, 1815-1819, 2019 | 88 | 2019 |
Association between Changes in Mammographic Image Features and Risk for Near-term Breast Cancer Development M Tan, B Zheng, J Leader, D Gur IEEE Transactions on Medical Imaging, 2016 | 88 | 2016 |
Applying a new quantitative global breast MRI feature analysis scheme to assess tumor response to chemotherapy F Aghaei, M Tan, AB Hollingsworth, B Zheng Journal of Magnetic Resonance Imaging 44 (5), 1099-1106, 2016 | 78 | 2016 |
Prediction of near-term breast cancer risk based on bilateral mammographic feature asymmetry M Tan, B Zheng, P Ramalingam, D Gur Academic radiology 20 (12), 1542-1550, 2013 | 63 | 2013 |
Assessment of a four-view mammographic image feature based fusion model to predict near-term breast cancer risk M Tan, J Pu, S Cheng, H Liu, B Zheng Annals of biomedical engineering 43, 2416-2428, 2015 | 61 | 2015 |
Computer‐aided breast MR image feature analysis for prediction of tumor response to chemotherapy F Aghaei, M Tan, AB Hollingsworth, W Qian, H Liu, B Zheng Medical Physics 42 (11), 6520-6528, 2015 | 57 | 2015 |
Gated-dilated networks for lung nodule classification in CT scans M Al-Shabi, HK Lee, M Tan IEEE Access 7, 178827-178838, 2019 | 54 | 2019 |
ProCAN: Progressive growing channel attentive non-local network for lung nodule classification M Al-Shabi, K Shak, M Tan Pattern Recognition 122, 108309, 2022 | 48 | 2022 |
A new approach to develop computer-aided detection schemes of digital mammograms M Tan, W Qian, J Pu, H Liu, B Zheng Physics in Medicine & Biology 60 (11), 4413, 2015 | 45 | 2015 |
Early prediction of clinical benefit of treating ovarian cancer using quantitative CT image feature analysis Y Qiu, M Tan, S McMeekin, T Thai, K Ding, K Moore, H Liu, B Zheng Acta Radiologica 57 (9), 1149-1155, 2016 | 41 | 2016 |
Reduction of false-positive recalls using a computerized mammographic image feature analysis scheme M Tan, J Pu, B Zheng Physics in Medicine & Biology 59 (15), 4357, 2014 | 41 | 2014 |
Computer-aided classification of mammographic masses using the deep learning technology: a preliminary study Y Qiu, S Yan, M Tan, S Cheng, H Liu, B Zheng Medical Imaging 2016: Computer-Aided Diagnosis 9785, 511-516, 2016 | 38 | 2016 |
Phased searching with NEAT in a time-scaled framework: experiments on a computer-aided detection system for lung nodules M Tan, R Deklerck, J Cornelis, B Jansen Artificial intelligence in medicine 59 (3), 157-167, 2013 | 37 | 2013 |
Association between computed tissue density asymmetry in bilateral mammograms and near‐term breast cancer risk B Zheng, M Tan, P Ramalingam, D Gur The breast journal 20 (3), 249-257, 2014 | 34 | 2014 |
Automated feature selection in neuroevolution M Tan, M Hartley, M Bister, R Deklerck Evolutionary Intelligence 1, 271-292, 2009 | 33 | 2009 |
A new approach to evaluate drug treatment response of ovarian cancer patients based on deformable image registration M Tan, Z Li, Y Qiu, SD McMeekin, TC Thai, K Ding, KN Moore, H Liu, ... IEEE Transactions on Medical Imaging 35 (1), 316-325, 2016 | 30 | 2016 |