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2020 – today
- 2024
[i3]Asim Waqas, Aakash Tripathi, Sabeen Ahmed, Ashwin Mukund, Hamza Farooq, Matthew B. Schabath, Paul Stewart, Mia Naeini, Ghulam Rasool:
SeNMo: A Self-Normalizing Deep Learning Model for Enhanced Multi-Omics Data Analysis in Oncology. CoRR abs/2405.08226 (2024)- [i2]Nikolas Koutsoubis, Yasin Yilmaz, Ravi P. Ramachandran, Matthew B. Schabath, Ghulam Rasool:
Privacy Preserving Federated Learning in Medical Imaging with Uncertainty Estimation. CoRR abs/2406.12815 (2024) - [i1]Nikolas Koutsoubis, Asim Waqas, Yasin Yilmaz, Ravi P. Ramachandran, Matthew B. Schabath, Ghulam Rasool:
Future-Proofing Medical Imaging with Privacy-Preserving Federated Learning and Uncertainty Quantification: A Review. CoRR abs/2409.16340 (2024) - 2023
- [c10]Nikolai Fetisov, Lawrence O. Hall, Dmitry B. Goldgof, Matthew B. Schabath:
Unsupervised Prostate Cancer Histopathology Image Segmentation via Meta-Learning. CBMS 2023: 838-844 - 2022
- [j3]Albert Rosenberger
, Viola Tozzi, Heike Bickeböller, Rayjean J. Hung, David C. Christiani, Neil E. Caporaso, Geoffrey Liu, Stig E. Bojesen, Loic Le Marchand, Demetrios Albanes, Melinda C. Aldrich, Adonina Tardon, Guillermo Fernández-Tardón, Gad Rennert, John K. Field, Mike Davies, Triantafillos Liloglou, Lambertus A. Kiemeney, Philip Lazarus, Aage Haugen, Shanbeh Zienolddiny, Stephen Lam, Matthew B. Schabath, Angeline S. Andrew, Eric J. Duell, Susanne M. Arnold, Hans Brunnström, Olle Melander, Gary E. Goodman, Chu Chen, Jennifer A. Doherty, Marion Dawn Teare, Angela Cox, Penella J. Woll, Angela Risch, Thomas R. Muley, Mikael Johansson, Paul Brennan, Maria Teresa Landi, Sanjay Shete, Christopher I. Amos:
Iam hiQ - a novel pair of accuracy indices for imputed genotypes. BMC Bioinform. 23(1): 50 (2022) - 2021
- [c9]Rahul Paul, Sherzod Kariev, Dmitry Cherezov, Matthew B. Schabath, Robert J. Gillies, Lawrence O. Hall, Dmitry B. Goldgof:
Deep radiomics: deep learning on radiomics texture images. Medical Imaging: Computer-Aided Diagnosis 2021 - 2020
- [j2]Rahul Paul, Matthew B. Schabath, Robert J. Gillies, Lawrence O. Hall, Dmitry B. Goldgof:
Convolutional Neural Network ensembles for accurate lung nodule malignancy prediction 2 years in the future. Comput. Biol. Medicine 122: 103882 (2020) - [c8]Rahul Paul, Matthew B. Schabath, Robert J. Gillies, Lawrence O. Hall, Dmitry B. Goldgof:
Mitigating Adversarial Attacks on Medical Image Understanding Systems. ISBI 2020: 1517-1521
2010 – 2019
- 2019
- [c7]Rahul Paul, Dmitry Cherezov, Matthew B. Schabath, Robert J. Gillies, Lawrence O. Hall, Dmitry B. Goldgof:
Towards deep radiomics: nodule malignancy prediction using CNNs on feature images. Medical Imaging: Computer-Aided Diagnosis 2019: 109503Z - 2018
- [j1]Saeed S. Alahmari, Dmitry Cherezov, Dmitry B. Goldgof, Lawrence O. Hall, Robert J. Gillies, Matthew B. Schabath:
Delta Radiomics Improves Pulmonary Nodule Malignancy Prediction in Lung Cancer Screening. IEEE Access 6: 77796-77806 (2018) - [c6]Rahul Paul, Lawrence O. Hall, Dmitry B. Goldgof, Matthew B. Schabath, Robert J. Gillies:
Predicting Nodule Malignancy using a CNN Ensemble Approach. IJCNN 2018: 1-8 - [c5]Rahul Paul, Ying Liu, Qian Li, Lawrence O. Hall, Dmitry B. Goldgof, Yoganand Balagurunathan, Matthew B. Schabath, Robert J. Gillies:
Representation of Deep Features using Radiologist defined Semantic Features. IJCNN 2018: 1-7 - [c4]Wei Mu, Jin Qi, Hong Lu, Matthew B. Schabath, Yoganand Balagurunathan, Ilke Tunali, Robert James Gillies:
Radiomic biomarkers from PET/CT multi-modality fusion images for the prediction of immunotherapy response in advanced non-small cell lung cancer patients. Medical Imaging: Computer-Aided Diagnosis 2018: 105753S - 2016
- [c3]Qian Li, Yoganand Balagurunathan, Ying Liu, Matthew B. Schabath, Robert J. Gillies:
Performance comparison of quantitative semantic features and lung-RADS in the National Lung Screening Trial. Medical Imaging: Image Perception, Observer Performance, and Technology Assessment 2016: 97870H - [c2]Ying Liu, Yoganand Balagurunathan, Thomas Atwater, Sanja Antic, Qian Li, Ronald Walker, Gary T. Smith, Pierre P. Massion, Matthew B. Schabath, Robert J. Gillies:
Quantitative imaging features to predict cancer status in lung nodules. Medical Imaging: Image Perception, Observer Performance, and Technology Assessment 2016: 97870L - [c1]Dmitry Cherezov, Samuel H. Hawkins, Dmitry B. Goldgof, Lawrence O. Hall, Yoganand Balagurunathan, Robert J. Gillies, Matthew B. Schabath:
Improving malignancy prediction through feature selection informed by nodule size ranges in NLST. SMC 2016: 1939-1944
Coauthor Index
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