T. DeSilvio

Deep hybrid convolutional wavelet networks: application to predicting response to chemoradiation in rectal cancers via MRI

D. Líška, S. Krishnamurthi, S. Viswanath, 2022, Medical Imaging.

A review of machine learning in obesity

D M Thomas, Jonathan W. Roginski, S B Heymsfield, 2018, Obesity reviews : an official journal of the International Association for the Study of Obesity.

Residual Wavelon Convolutional Networks for Characterization of Disease Response on MRI

S. Viswanath, A. Sadri, T. DeSilvio, 2022, International Conference on Medical Image Computing and Computer-Assisted Intervention.

Integrating multi-plane and multi-region radiomic features to predict pathologic response to neoadjuvant chemoradiation in rectal cancers via pre-treatment MRI

D. Líška, S. Krishnamurthi, S. Viswanath, 2023, Medical Imaging.

Region-specific deep learning models for accurate segmentation of rectal structures on post-chemoradiation T2w MRI: a multi-institutional, multi-reader study

D. Líška, K. Bera, S. Viswanath, 2023, Frontiers in Medicine.

Region-specific fully convolutional networks for segmentation of the rectal wall on post-chemoradiation T2w MRI

Jacob Antunes, Kaustav Bera, Raj M. Paspulati, 2019, Medical Imaging.

CohortFinder: an open-source tool for data-driven partitioning of digital pathology and imaging cohorts to yield robust machine-learning models

Maxime W. Lafarge, A. Madabhushi, A. Janowczyk, 2024, Npj imaging.

CohortFinder: an open-source tool for data-driven partitioning of biomedical image cohorts to yield robust machine learning models

Maxime W. Lafarge, A. Madabhushi, A. Janowczyk, 2023, ArXiv.