Joshua Roccamo
Undergraduate student
B.Sc. Computer Science
Artificial Intelligence Group
+1 (514) 4655687
Bio
Joshua is a B.Sc. student in computer science at McGill university, where he also does research in the AI subgroup of the Enger lab. Joshua is originally from Ottawa and has experience in biochemistry. Currently, Joshua is working to use AI to segment organs in brachytherapy and to identify malignant pancreatic cysts. In his free time, Joshua enjoys reading and coding.
Current Projects
Deep learning-based Organ Auto-Segmentation in Brachytherapy
Joshua is developing a UNET deep learning-based organ segmentation algorithm on CT images for brachytherapy.
Artificial intelligence with radiomics and deep learning for the diagnosis of malignancy in pancreatic cysts: a pilot study
Pancreatic cysts are common, with a prevalence of 15% in the general population, and up to 37% in older populations. While some cystic lesions of the pancreas, such as serous cystadenomas, pseudocysts and epidermoid cysts, have little to no malignant potential, others including intraductal papillary mucinous neoplasm (IPMN) and mucinous cystic neoplasm carry a risk of invasive adenocarcinoma. Even among the potentially malignant cyst types, it is difficult to predict which cysts harbor malignancy or have a high risk of malignant transformation. In this project, we aim to 1) develop a deep learning algorithm using radiomics from EUS, CT and MRI images of pancreas cysts to predict malignancy; 2) To assess the diagnostic ability of this prediction algorithm in differentiating malignant from benign pancreatic cysts; 3) To compare the diagnostic ability of this AI algorithm to standard of care criteria for predicting malignant pancreatic cystic neoplasms; 4) To assess feasibility of the current methodology and AI algorithm for a large multicenterd trial with an external validation cohort.