Hi there! My name is Thomas DeSilvio, and I am a biomedical engineering PhD student at Case Western Reserve University, Cleveland OH.


My Research

My research focuses on developing novel artificial intelligence, specifically deep learning, tools for medical imaging analysis to improve computer-aided diagnosis and treatment evaluation. By applying these tools to magnetic resonance imaging (MRI) and digital pathology, I hope to enhance cancer detection, intervention, and treatment evaluation for patients around the world.

I work in the Center for Computational Imaging and Personalized Diagnostics and Imaging Informatics for Interventions (INVent) Lab at Case Western Reserve University, Cleveland OH.


Journal Publications

DeSilvio T, Antunes JT, Bera K, Chirra P, Le H, Liska D, Stein SL, Marderstein E, Hall W, Paspulati R, Gollamudi J, Purysko AS and Viswanath SE (2023) Region-specific deep learning models for accurate segmentation of rectal structures on post-chemoradiation T2w MRI: a multi-institutional, multi-reader study. Front. Med. 10:1149056. doi: 10.3389/fmed.2023.1149056

Singh S, DeSilvio T , Sadri A, Labbad M, Gupta A, Bingmer K, Paspulati R, Gollamudi J, Friedman A, Liska D, Stein S, Marderstein E, PuryskoA, Krishnamurthi S, and Viswanath S.E., Radiomic tumor diversity features across pre- and post-chemoradiation MRI are associated with pathologic complete response to neoadjuvant chemoradiation in rectal cancers: a multi-institutional study (under review)

Fan F, Martinez G, DeSilvio T, Shin J, Chen Y, Wang W, Ozeki T, Lafarge M, Koelzer V, Barisoni L, Madabhushi A, Viswanath S.E, & Janowczyk A. (2023). CohortFinder: an open-source tool for data-driven partitioning of digital pathology and imaging cohorts to yield robust machine-learning models. npj Imaging 2, 15 (2024). https://doi.org/10.1038/s44303-024-00018-2

Alvarez-Jimenez C, Antunes J, Wei Z, DeSilvio T, Ismail M, Madabhushi A, Romero E, Tiwari P, Viswanath S.E. A Novel STrUctural DEformationand Orientation (STuDeO) Radiomic Descriptor to Quantify Chemoradiation Response on MRI: Application to Rectal Cancers. Medical Image Analysis (under review) 

DeGregory, K. W.,  Kuiper, P.,  DeSilvio, T.,  Pleuss, J. D.,  Miller, R.,  Roginski, J. W.,  Fisher, C. B.,  Harness, D., Viswanath, S.,  Heymsfield, S. B.,  Dungan, I., and  Thomas, D. M. (2018)  A review of machine learning in obesity. Obesity Reviews,  19:  668– 685. doi: 10.1111/obr.12667


Conference Publications

Sridharan A., Hariri M., Kong M., Flannery B., DeSilvio T., Elumalai A., Devi J., Lovato A., Maneiro C., George A. T., Ganapathy A., Deepak P., Ballard D. H., Viswanath S. E. Empirical comparison of self-configuring and foundational deep learning segmentation models for identifying the anal sphincter complex and perianal fistulas on pelvic MRI. SPIE Medical Imaging 2025.

Bao L, DeSilvio T, Parker BN, Hariri M, Chirra P, Tang S, O’Connor GM, Steinhagen E, Miller-Ocuin J, Gupta A, Carroll A, Crittenden M, Gough MJ, Krishnamurthi S, Young KH, Marderstein EL, Viswanath SE. Machine Learning Analysis of Image Features on Baseline MRI Can Identify Responders to Multiple Neoadjuvant Therapy Regimen in Rectal Cancers. Oral presentation at: Association of VA Surgeons Annual Meeting; April 8, 2024; Miami, FL. (oral presentation)

Parker B, Kong M, DeSilvio T, Bao L, Flannery B, Tang S, O’Connor G, Gupta A, Steinhagen E, Purysko A, Carroll A, Crittenden M, Gough M, Young K, Marderstein E, Viswanath S. Automated Annotation of Rectal Tumors on MRI Using Human-Informed Deep Learning. Oral presentation at: Association of VA Surgeons Annual Meeting; April 8, 2024; Miami, FL. (oral presentation)

DeSilvio T* and Kong M*, Bao L., Flannery B., Parker B. N., Tang S., O’Connor G., Gupta A., Steinhagen E., Purysko A. S., Marderstein E. L., Carroll A., Crittenden M., Gough M., Young K., Viswanath S. E.; Abstract 7386: Multi-plane rectal tumor segmentation on pre-treatment MRI via human-in-the-loop deep learning. Cancer Res 15 March 2024; 84 (6_Supplement): 7386. https://doi.org/10.1158/1538-7445.AM2024-7386 *indicates co-first authors (poster)

Flannery B., Hariri M., DeSilvio T., Sadri A., Nguyen J., Remer E. M., Krishnamurthi S., Viswanath S. E.; Abstract 7379: Deep learning based risk stratification of pre-operative CT scans is prognostic of overall survival in kidney cancers. Cancer Res 15 March 2024; 84 (6_Supplement): 7379. https://doi.org/10.1158/1538-7445.AM2024-7379 (poster)

Bao L., DeSilvio T., Parker B. N., Hariri M., Chirra P., Labbad M., Tang S., O’Connor G. M., Steinhagen E., Miller-Ocuin J. L., Gupta A., Marderstein E. L., Carroll A., Crittenden M., Gough M. J., Krishnamurthi S., Young K. H., Viswanath S. E.; Abstract 2582: Intra- and peri-tumoral radiomic features are predictive of pathologic response to multiple neoadjuvant therapy regimen in rectal cancers via pre-treatment MRI. Cancer Res 15 March 2024; 84 (6_Supplement): 2582https://doi.org/10.1158/1538-7445.AM2024-2582 (poster)

DeSilvio T* and Kong M*, Bao L, Flannery B, Parker BN, Tang SM, O’Connor GM, Gupta A, Steinhagen E, Purysko A, Marderstain EL, Carroll A, Crittenden M, Gough M, Young KH, Viswanath SE. “Human-in-the-loop informed deep learning rectal tumor segmentation on pre-treatment MRI,” Proc. SPIE 12927, Medical Imaging 2024: Computer-Aided Diagnosis, 1292707 (3 April 2024); https://doi.org/10.1117/12.3008637 *indicates co-first authors (oral presentation).

Flannery F, DeSilvio T, Sadri AR, Hariri M, Remer EM, Nguyen J, Viswanath SE. “Spatial attention wavelon network (SpAWN) for survival-based risk stratification in kidney cancers via CT,” Proc. SPIE 12927, Medical Imaging 2024: Computer-Aided Diagnosis, 129272E (3 April 2024); https://doi.org/10.1117/12.3008727 (poster).

DeSilvio T, Bao L, Seth D, Chirra P, Singh S, Sridharan A, Labbad M, Bingmer K, Jodeh D, Marderstein EL, Paspulati R, Liska D, Friedman KA, Krishnamurthi S, Stein SL, Purysko AS, Viswanath SE. Integrating multi-plane and multi-region radiomic features to predict pathologic response to neoadjuvant chemoradiation in rectal cancers via pre-treatment MRI. 2023. p. 124660K. Available from: https://doi.org/10.1117/12.2655787 doi:10.1117/12.2655787 (oral presentation)

Le H, DeSilvio T, Patel S, Vasilyeva D, Pathak T, Friedman K A, Stein S L, Viswanath SE, “Computerized Pathomic Descriptors of Residual Tumor on Digitized Pathology Specimens for Evaluation of Tumor Stage and Regression Grade after Neoadjuvant Chemoradiation in Rectal Cancers”, ASCRS 2023 (poster)

Singh S, DeSilvio T, Sadri A, Labbad M, Bingmer K, Paspulati R, Friedman K A, Liska D, Stein S L, Marderstein E, Krishnamurthi S,  Purysko A, Viswanath SE, “Tumor diversity features across pre- and post-chemoradiation MRI are associated with degree of pathologic response to chemoradiation in rectal cancers: a multi-institution study”, RSNA 2022 (poster)

Sadri A, DeSilvio T, Debnath T, Toth R, Bera B, Gupta A, Soman S, Nayate A, Hill V, Tiwari P, and Viswanath SE, “A light-weight deep learning web app for rapid identification of poor-quality structural MRI scans: A multi-institutional study”, RSNA 2022 (poster)

Sadri A, DeSilvio T, Debnath T, Toth R, Bera B, Gupta A, Soman S, Nayate A, Hill V, Tiwari P, and Viswanath SE, “Unsupervised identification of MRI artifacts via integration of deep learning and image quality measures”, RSNA 2022 (poster)

Singh S, DeSilvio T, Purysko A, Paspulati R, Friedman K A, Liska D, Stein S L, Krishnamurthi S, Viswanath SE, “Computerized features of tumor diversity on pre-chemoradiation MRI predict pathologic complete response in rectal cancers: A multi-institutional study”, ASCO 2022 (poster)

Sadri AR, DeSilvio T, Chirra P, Singh S, Viswanath SE, Residual Wavelon Convolutional Networks for Characterization of Disease Response on MRI. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds) Medical Image Computing and Computer Assisted Intervention – MICCAI 2022. MICCAI 2022. Lecture Notes in Computer Science, vol 13433. Springer, Cham. https://doi.org/10.1007/978-3-031-16437-8_35 (oral)

Sadri, AR, DeSilvio, T, Purysko, A, Paspulati, R,  Friedman, KA, Krishnamurthi, SS, Liska, D, Stein, SL, and Viswanath, SE, “Deep Hybrid Convolutional Wavelet Networks: Application to Predicting Response to Chemoradiation in Rectal Cancers via MRI”, SPIE Medical Imaging, 2022 (oral)

DeSilvio T, Moroianu S, Bhattacharya I, Seetharaman A, Sonn G, Rusu M, “Intensity normalization of prostate MRIs using conditional generative adversarial networks for cancer detection,” Proc. SPIE 11597, Medical Imaging 2021: Computer-Aided Diagnosis, 115970J (15 February 2021); https://doi.org/10.1117/12.2582297 (oral)

DeSilvio T, Antunes J, Chirra P, Bera K, Gollamudi J, Paspulati R, Delaney C, Viswanath S.E>, “Region-specific fully convolutional networks for segmentation of the rectal wall on post-chemoradiation T2w MRI,” Proc. SPIE 10951, Medical Imaging 2019: Image-Guided Procedures, Robotic Interventions, and Modeling, 1095134 (8 March 2019); https://doi.org/10.1117/12.2513055 (poster)


Skills

 PythonMATLABDeep LearningComputer Vision
PyTorchKerasTensorflowDocker
GithubImage ProcessingHigh Performance/Distributed ComputingMedical Image Analysis

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