Mr. Giacomo Nebbia Profile

Giacomo Nebbia

at Univ of Pittsburgh

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Proceedings Article | 15 February 2021 Presentation + Paper

Multi-task learning to incorporate clinical knowledge into deep learning for breast cancer diagnosis

Giacomo Nebbia, Dooman Arefan, Margarita Zuley, Jules Sumkin, Shandong Wu

Proceedings Volume 11597, 1159710 (2021) https://doi.org/10.1117/12.2582285

KEYWORDS: Breast cancer, Tumor growth modeling, Data modeling, Cancer, Performance modeling, Mammography, Digital mammography, Breast

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Deep learning models are traditionally trained purely in a data-driven approach; the information for the model training usually only comes from a single source of the training data. In this work, we investigate how to supply additional clinical knowledge that is associated with the training data. Our goal is to train deep learning models for breast cancer diagnosis using mammogram images. Along with the main classification task between clinically proven cancer vs negative/benign cases, we design two auxiliary tasks each capturing a form of additional knowledge to facilitate the main task. Specifically, one auxiliary task is to classify images according to the radiologist-made BI-RADS diagnosis scores and the other auxiliary task is to classify images in terms of the BI-RADS breast density categories. We customize a Multi-Task Learning model to jointly perform the three tasks (main task and two auxiliary tasks). We test four deep learning architectures: CBR–Tiny, ResNet18, GoogleNet, and DenseNet and we investigate the benefit of incorporating such knowledge over ImageNet pre-trained models and in the case of randomly initialized models. We run experiments on an internal dataset consisting of screening full field digital mammography images for a total of 1,380 images (341 cancer and 1,039 negative or benign). Our results show that, by adding clinical knowledge conveyed through the two auxiliary tasks to the training process, we can improve the performance of the target task of breast cancer diagnosis, thus highlighting the benefit of incorporating clinical knowledge into data-driven learning to enhance deep learning model training.

Proceedings Article | 13 March 2019 Paper

Deep learning of sub-regional breast parenchyma in mammograms for localized breast cancer risk prediction

Giacomo Nebbia, Aly Mohamed, Ruimei Chai, Bingjie Zheng, Margarita Zuley, Shandong Wu

Proceedings Volume 10950, 109502P (2019) https://doi.org/10.1117/12.2512943

KEYWORDS: Machine learning, Convolutional neural networks, Breast, Breast cancer, Mammography, Cancer, Tumors, Radiology, Intelligence systems, Breast imaging

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Breast cancer risk prediction refers to the task of predicting whether a healthy patient is likely to develop breast cancer in the future. Breast density and parenchymal texture features are well-known imaging-based breast cancer risk markers that can be qualitatively/visually assessed by radiologists or even quantitatively measured by computerized software. Recently, deep learning has emerged as a promising strategy to solve tasks in a variety of classification and prediction scenarios, including breast imaging. Building on this premise, we propose a deep learning-based modeling method for breast cancer risk prediction in a case-control setting purely using prior normal screening mammogram images. In addition, considering the fact that clinical statistics shows that the upper outer quadrant is the most common site of origin for breast cancer, we designed a simple experiment on 226 patients (a total of 1,632 images) to explore the concept of localized breast cancer risk prediction. We built two deep learning models with the same settings but fed one with the top halves of the mammogram images (corresponding to the outer portion of a breast) and the other with the bottom halves (corresponding to the inner portion of a breast). Our preliminary results showed that the top halves have a higher prediction performance (AUC=0.89) than the bottom halves (AUC=0.69) in predicting the case/control outcome. This indicates a relation between localized imaging features extracted from a sub-region of the full mammogram images and the underlying risk of developing breast cancer in this specific sub-region.

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