BIO

I'm a PhD candidate in the Intelligent Systems Program - Biomedical Informatics track at the University of Pittsburgh.

My research interests are in computer vision for medical and non-medical applications.

I am currently working in the Pitt Computer Vision Lab at the University of Pittsburgh, supervised by Dr. Adriana Kovashka.

Some of the projects I have worked/am working on include

Multi-modal grounding for supervised and weakly-supervised object detection
Inclusion of clinical knowledge in deep learning for breast cancer diagnosis from mammography using transfer learninig, multi-task learning, and curriculum learning.
Radiomics and Deep Learning approaches to predict microvascular invasion (mVI) of HepatoCellular Carncinoma (HCC) from multiphase MRI

PUBLICATIONS

Giacomo Nebbia, Adriana Kovashka; "Doubling down: sparse grounding with an additional, almost-matching caption for detection-oriented multimodal pretraining.", Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2022, pp. 4642-4651
Giacomo Nebbia, Saba Dadsetan, Dooman Arefan, Margarita L. Zuley, Jules H. Sumkin, Heng Huang, and Shandong Wu. "Radiomics-Informed Deep Curriculum Learning for Breast Cancer Diagnosis." In International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 634-643. Springer, Cham, 2021.
Saba Dadsetan, Giacomo Nebbia, Dooman Arefan, Wendie A. Berg, Margarita Zuley, Jules Submkin, Shandong Wu. LRP-NET: A Deep Learning Model on Longitudinal Screening Mammograms for Breast Cancer Risk Prediction. Under review
Dadashzadeh, Esmaeel Reza, Patrick Bou-Samra, Lauren V. Huckaby, Giacomo Nebbia, Robert M. Handzel, Patrick R. Varley, Shandong Wu, and Allan Tsung. "Leveraging Decision Curve Analysis to Improve Clinical Application of Surgical Risk Calculators." Journal of Surgical Research 261 (2021): 58-66. https://doi.org/10.1016/j.jss.2020.11.059
Giacomo Nebbia,Qian Zhang, Dooman Arefan, Xinxiang Zhao, and Shandong Wu. "Pre-operative Microvascular Invasion Prediction Using Multi-parametric Liver MRI Radiomics." Journal of Digital Imaging (2020). https://doi.org/10.1007/s10278-020-00353-x
Giacomo Nebbia, Lisa Nussbaum, Annie Helmkamp, Stacy Andersen, Thomas Perls & Paola Sebastiani (2018). Manual and Automated Procedures for Compiling a Very Large Sample of Centenarian Pedigrees, North American Actuarial Journal, DOI: 10.1080/10920277.2018.1462716

PRESENTATIONS

Giacomo Nebbia, Dooman Arefan, Margarita Zuley, Jules Sumkin, Shandong Wu. Multi-task learning to incorporate clinical knowledge into deep learning for breast cancer diagnosis. SPIE Medical Imaging. Feb. 14-18, 2021
Jacob Yousef, Giacomo Nebbia, Roberta Catania, Biatta Sholosh, Senthur Thangasamy, Kalina Chupetlovska, Satdarshan P Monga, Shandong Wu, Alessandro Furlan. Multivariate analysis of radiological predictors of beta catenin mutation status in hepatocellular carcinoma (HCC) according to the Liver Imaging Reporting and Data System (LI-RADS), European Congress of Radiology, March, 3-7 2021
Rafael Ramos, Esmaeel Dadashzadeh, Giacomo Nebbia, Graciela Bauza, Shandong Wu (2019) Rib Fracture Patterns Associated with Diaphragmatic Injury: A Retrospective Review. 2019 Chest Wall Injury Summit, March 28-30, Santa Fe (NM)
Giacomo Nebbia, Aly A. Mohamed, Ruimei Chai, Bingjie Zheng, Margarita Zuley, Shandong Wu (2019) Deep learning of sub-regional breast parenchyma in mammograms for localized breast cancer risk prediction. Poster at the 2019 SPIE Computer-Aided Diagnosis conference, February 17-20, San Diego (CA).
Giacomo Nebbia, Esmaeel Dadashzadeh, Caroline Rieser, Shandong Wu (2019) Going beyond MELD: A data-driven mortality predictor for liver transplantation waiting list. 14th Annual Academic Surgical Congress, February 5-7 2019, Houston (TX)

GIACOMO NEBBIA

BIO

I'm a PhD candidate in the Intelligent Systems Program - Biomedical Informatics track at the University of Pittsburgh.

My research interests are in computer vision for medical and non-medical applications.

I am currently working in the Pitt Computer Vision Lab at the University of Pittsburgh, supervised by Dr. Adriana Kovashka.

Some of the projects I have worked/am working on include

Multi-modal grounding for supervised and weakly-supervised object detection

Inclusion of clinical knowledge in deep learning for breast cancer diagnosis from mammography using transfer learninig, multi-task learning, and curriculum learning.

Radiomics and Deep Learning approaches to predict microvascular invasion (mVI) of HepatoCellular Carncinoma (HCC) from multiphase MRI

PUBLICATIONS

Giacomo Nebbia, Adriana Kovashka; "Doubling down: sparse grounding with an additional, almost-matching caption for detection-oriented multimodal pretraining.", Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2022, pp. 4642-4651

Saba Dadsetan, Giacomo Nebbia, Dooman Arefan, Wendie A. Berg, Margarita Zuley, Jules Submkin, Shandong Wu. LRP-NET: A Deep Learning Model on Longitudinal Screening Mammograms for Breast Cancer Risk Prediction. Under review

Giacomo Nebbia,Qian Zhang, Dooman Arefan, Xinxiang Zhao, and Shandong Wu. "Pre-operative Microvascular Invasion Prediction Using Multi-parametric Liver MRI Radiomics." Journal of Digital Imaging (2020). https://doi.org/10.1007/s10278-020-00353-x

Giacomo Nebbia, Lisa Nussbaum, Annie Helmkamp, Stacy Andersen, Thomas Perls & Paola Sebastiani (2018). Manual and Automated Procedures for Compiling a Very Large Sample of Centenarian Pedigrees, North American Actuarial Journal, DOI: 10.1080/10920277.2018.1462716

PRESENTATIONS

Giacomo Nebbia, Dooman Arefan, Margarita Zuley, Jules Sumkin, Shandong Wu. Multi-task learning to incorporate clinical knowledge into deep learning for breast cancer diagnosis. SPIE Medical Imaging. Feb. 14-18, 2021

Rafael Ramos, Esmaeel Dadashzadeh, Giacomo Nebbia, Graciela Bauza, Shandong Wu (2019) Rib Fracture Patterns Associated with Diaphragmatic Injury: A Retrospective Review. 2019 Chest Wall Injury Summit, March 28-30, Santa Fe (NM)

Giacomo Nebbia, Aly A. Mohamed, Ruimei Chai, Bingjie Zheng, Margarita Zuley, Shandong Wu (2019) Deep learning of sub-regional breast parenchyma in mammograms for localized breast cancer risk prediction. Poster at the 2019 SPIE Computer-Aided Diagnosis conference, February 17-20, San Diego (CA).

Giacomo Nebbia, Esmaeel Dadashzadeh, Caroline Rieser, Shandong Wu (2019) Going beyond MELD: A data-driven mortality predictor for liver transplantation waiting list. 14th Annual Academic Surgical Congress, February 5-7 2019, Houston (TX)

CONTACTS

Email address: gin2@pitt.edu

LinkedIn: Giacomo Nebbia

CV: Open