Mu Zhou
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Multi-scale convolutional neural networks for lung nodule classification
W Shen, M Zhou, F Yang, C Yang, J Tian
International Conference on Information Processing in Medical Imaging, 588-599, 2015
Multi-crop Convolutional Neural Networks for lung nodule malignancy suspiciousness classification
W Shen, M Zhou, F Yang, D Yu, D Dong, C Yang, Y Zang, J Tian
Pattern Recognition 61, 663-673, 2017
Central focused convolutional neural networks: Developing a data-driven model for lung nodule segmentation
S Wang, M Zhou, Z Liu, Z Liu, D Gu, Y Zang, D Dong, O Gevaert, J Tian
Medical Image Analysis 40, 172-183, 2017
Radiomics in Brain Tumor: Image Assessment, Quantitative Feature Descriptors, and Machine-Learning Approaches
M Zhou, J Scott, B Chaudhury, L Hall, D Goldgof, KW Yeom, M Iv, Y Ou, ...
American Journal of Neuroradiology, 2017
Predicting EGFR mutation status in lung adenocarcinoma on computed tomography image using deep learning
S Wang, J Shi, Z Ye, D Dong, D Yu, M Zhou, Y Liu, O Gevaert, K Wang, ...
European Respiratory Journal 53 (3), 1800986, 2019
Facial action unit recognition with sparse representation
MH Mahoor, M Zhou, KL Veon, SM Mavadati, JF Cohn
Automatic Face & Gesture Recognition and Workshops (FG 2011), 2011 IEEE …, 2011
Non–small cell lung cancer radiogenomics map identifies relationships between molecular and imaging phenotypes with prognostic implications
M Zhou, A Leung, S Echegaray, A Gentles, JB Shrager, KC Jensen, ...
Radiology 286 (1), 307-315, 2018
Radiologically defined ecological dynamics and clinical outcomes in glioblastoma multiforme: preliminary results
M Zhou, L Hall, D Goldgof, R Russo, Y Balagurunathan, R Gillies, ...
Translational oncology 7 (1), 5-13, 2014
A radiogenomic dataset of non-small cell lung cancer
S Bakr, O Gevaert, S Echegaray, K Ayers, M Zhou, M Shafiq, H Zheng, ...
Scientific data 5 (1), 1-9, 2018
Identifying spatial imaging biomarkers of glioblastoma multiforme for survival group prediction
M Zhou, B Chaudhury, LO Hall, DB Goldgof, RJ Gillies, RA Gatenby
Journal of Magnetic Resonance Imaging, 2016
A multi-view deep convolutional neural networks for lung nodule segmentation
S Wang, M Zhou, O Gevaert, Z Tang, D Dong, Z Liu, J Tian
2017 39th Annual International Conference of the IEEE Engineering in …, 2017
Learning from Experts: Developing Transferable Deep Features for Patient-Level Lung Cancer Prediction
W Shen, M Zhou, F Yang, D Dong, C Yang, Y Zang, J Tian
International Conference on Medical Image Computing and Computer-Assisted …, 2016
MR Imaging–Based Radiomic Signatures of Distinct Molecular Subgroups of Medulloblastoma
M Iv, M Zhou, K Shpanskaya, S Perreault, Z Wang, E Tranvinh, ...
American Journal of Neuroradiology 40 (1), 154-161, 2019
Heterogeneity in intratumoral regions with rapid gadolinium washout correlates with estrogen receptor status and nodal metastasis
B Chaudhury, M Zhou, DB Goldgof, LO Hall, RA Gatenby, RJ Gillies, ...
Journal of Magnetic Resonance Imaging 42 (5), 1421-1430, 2015
Non-invasive genotype prediction of chromosome 1p/19q co-deletion by development and validation of an MRI-based radiomics signature in lower-grade gliomas
Y Han, Z Xie, Y Zang, S Zhang, D Gu, M Zhou, O Gevaert, J Wei, C Li, ...
Journal of Neuro-Oncology, 1-10, 2018
Development and validation of radiomic signatures of head and neck squamous cell carcinoma molecular features and subtypes
C Huang, M Cintra, K Brennan, M Zhou, AD Colevas, N Fischbein, S Zhu, ...
EBioMedicine 45, 70-80, 2019
3-D Convolutional Neural Networks for Glioblastoma Segmentation
D Yi, M Zhou, Z Chen, O Gevaert
arXiv preprint arXiv:1611.04534, 2016
Data for NSCLC radiogenomics collection
S Bakr, O Gevaert, S Echegaray, K Ayers, M Zhou, M Shafiq, H Zheng, ...
The Cancer Imaging Archive, 2017
Quantitative imaging outperforms molecular markers when predicting response to chemoradiotherapy for rectal cancer
I Joye, A Debucquoy, CM Deroose, V Vandecaveye, E Van Cutsem, ...
Radiotherapy and Oncology 124 (1), 104-109, 2017
Automatic quantification and classification of cervical cancer via adaptive nucleus shape modeling
HA Phoulady, M Zhou, DB Goldgof, LO Hall, PR Mouton
2016 IEEE International Conference on Image Processing (ICIP), 2658-2662, 2016
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