Matthew B. Schabath

According to our database, Matthew B. Schabath authored at least 17 papers between 2016 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

Future-Proofing Medical Imaging with Privacy-Preserving Federated Learning and Uncertainty Quantification: A Review.

Nikolas Koutsoubis, Asim Waqas, Yasin Yilmaz, Ravi P. Ramachandran, Matthew B. Schabath, Ghulam Rasool

CoRR, 2024

Privacy Preserving Federated Learning in Medical Imaging with Uncertainty Estimation.

Nikolas Koutsoubis, Yasin Yilmaz, Ravi P. Ramachandran, Matthew B. Schabath, Ghulam Rasool

CoRR, 2024

SeNMo: A Self-Normalizing Deep Learning Model for Enhanced Multi-Omics Data Analysis in Oncology.

Asim Waqas, Aakash Tripathi, Sabeen Ahmed, Ashwin Mukund, Hamza Farooq, Matthew B. Schabath, Paul Stewart, Mia Naeini, Ghulam Rasool

CoRR, 2024

2023

Unsupervised Prostate Cancer Histopathology Image Segmentation via Meta-Learning.

Nikolai Fetisov, Lawrence O. Hall, Dmitry B. Goldgof, Matthew B. Schabath

Proceedings of the 36th IEEE International Symposium on Computer-Based Medical Systems, 2023

2022

Iam hiQ - a novel pair of accuracy indices for imputed genotypes.

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

BMC Bioinform., 2022

Survival time prediction from unannotated lung cancer histopathology images.

Nikolai Fetisov, Lawrence O. Hall, Dmitry B. Goldgof, Matthew B. Schabath

Proceedings of the Medical Imaging 2022: Digital and Computational Pathology, 2022

2021

Deep radiomics: deep learning on radiomics texture images.

Rahul Paul, Sherzod Kariev, Dmitry Cherezov, Matthew B. Schabath, Robert J. Gillies, Lawrence O. Hall, Dmitry B. Goldgof

Proceedings of the Medical Imaging 2021: Computer-Aided Diagnosis, 2021

2020

Convolutional Neural Network ensembles for accurate lung nodule malignancy prediction 2 years in the future.

Rahul Paul, Matthew B. Schabath, Robert J. Gillies, Lawrence O. Hall, Dmitry B. Goldgof

Comput. Biol. Medicine, 2020

Mitigating Adversarial Attacks on Medical Image Understanding Systems.

Rahul Paul, Matthew B. Schabath, Robert J. Gillies, Lawrence O. Hall, Dmitry B. Goldgof

Proceedings of the 17th IEEE International Symposium on Biomedical Imaging, 2020

2019

Towards deep radiomics: nodule malignancy prediction using CNNs on feature images.

Rahul Paul, Dmitry Cherezov, Matthew B. Schabath, Robert J. Gillies, Lawrence O. Hall, Dmitry B. Goldgof

Proceedings of the Medical Imaging 2019: Computer-Aided Diagnosis, San Diego, 2019

2018

Delta Radiomics Improves Pulmonary Nodule Malignancy Prediction in Lung Cancer Screening.

Saeed S. Alahmari, Dmitry Cherezov, Dmitry B. Goldgof, Lawrence O. Hall, Robert J. Gillies, Matthew B. Schabath

IEEE Access, 2018

Radiomic biomarkers from PET/CT multi-modality fusion images for the prediction of immunotherapy response in advanced non-small cell lung cancer patients.

Wei Mu, Jin Qi, Hong Lu, Matthew B. Schabath, Yoganand Balagurunathan, Ilke Tunali, Robert James Gillies

Proceedings of the Medical Imaging 2018: Computer-Aided Diagnosis, 2018

Representation of Deep Features using Radiologist defined Semantic Features.

Rahul Paul, Ying Liu, Qian Li, Lawrence O. Hall, Dmitry B. Goldgof, Yoganand Balagurunathan, Matthew B. Schabath, Robert J. Gillies

Proceedings of the 2018 International Joint Conference on Neural Networks, 2018

Predicting Nodule Malignancy using a CNN Ensemble Approach.

Rahul Paul, Lawrence O. Hall, Dmitry B. Goldgof, Matthew B. Schabath, Robert J. Gillies

Proceedings of the 2018 International Joint Conference on Neural Networks, 2018

2016

Improving malignancy prediction through feature selection informed by nodule size ranges in NLST.

Dmitry Cherezov, Samuel H. Hawkins, Dmitry B. Goldgof, Lawrence O. Hall, Yoganand Balagurunathan, Robert J. Gillies, Matthew B. Schabath

Proceedings of the 2016 IEEE International Conference on Systems, Man, and Cybernetics, 2016

Quantitative imaging features to predict cancer status in lung nodules.

Ying Liu, Yoganand Balagurunathan, Thomas Atwater, Sanja Antic, Qian Li, Ronald Walker, Gary T. Smith, Pierre P. Massion, Matthew B. Schabath, Robert J. Gillies

Proceedings of the Medical Imaging 2016: Image Perception, Observer Performance, and Technology Assessment, San Diego, California, United States, 27 February, 2016

Performance comparison of quantitative semantic features and lung-RADS in the National Lung Screening Trial.

Qian Li, Yoganand Balagurunathan, Ying Liu, Matthew B. Schabath, Robert J. Gillies

Proceedings of the Medical Imaging 2016: Image Perception, Observer Performance, and Technology Assessment, San Diego, California, United States, 27 February, 2016