| Lung nodule malignancy classification using only radiologist-quantified image features as inputs to statistical learning algorithms: probing the Lung Image Database Consortium … MC Hancock, JF Magnan Journal of Medical Imaging 3 (4), 044504-044504, 2016 | 92 | 2016 |
| Standardized representation of the LIDC annotations using DICOM A Fedorov, M Hancock, D Clunie, M Brochhausen, J Bona, J Kirby, ... PeerJ Preprints, 2019 | 15 | 2019 |
| Predictive capabilities of statistical learning methods for lung nodule malignancy classification using diagnostic image features: an investigation using the Lung Image … MC Hancock, JF Magnan Medical Imaging 2017: Computer-Aided Diagnosis 10134, 558-569, 2017 | 15 | 2017 |
| DICOM re‐encoding of volumetrically annotated Lung Imaging Database Consortium (LIDC) nodules A Fedorov, M Hancock, D Clunie, M Brochhausen, J Bona, J Kirby, ... Medical physics 47 (11), 5953-5965, 2020 | 12 | 2020 |
| Lung nodule segmentation via level set machine learning MC Hancock, JF Magnan arXiv preprint arXiv:1910.03191 410, 2019 | 12 | 2019 |
| Standardized representation of the TCIA LIDC-IDRI annotations using DICOM A Fedorov, M Hancock, D Clunie, M Brockhhausen, J Bona, J Kirby, ... Published online 10, 2018 | 5 | 2018 |
| Level set image segmentation with velocity term learned from data with applications to lung nodule segmentation MC Hancock, JF Magnan arXiv preprint arXiv:1910.03191, 2019 | 3 | 2019 |
| Algorithmic Lung Nodule Analysis in Chest Tomography Images: Lung Nodule Malignancy Likelihood Prediction and a Statistical Extension of the Level Set Image Segmentation Method MC Hancock The Florida State University, 2018 | | 2018 |
| Sciatic Nerve Segmentation in MRI Volumes of the Upper Leg via 3D Convolutional Neural Networks M Hancock, S Manjunath, J Li, R Dortch | | |
