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Weakly Supervised 3D Classification of Chest CT using Aggregated Multi-Resolution Deep Segmentation Features
Anindo Saha, Fakrul I. Tushar, Khrystyna Faryna +5
Weakly supervised disease classification of CT imaging suffers from poor localization owing to case-level annotations, where even a positive scan can hold hundreds to thousands of…
Classification of Multiple Diseases on Body CT Scans using Weakly Supervised Deep Learning
Fakrul Islam Tushar, Vincent M. D'Anniballe, Rui Hou +5
Purpose: To design multi-disease classifiers for body CT scans for three different organ systems using automatically extracted labels from radiology text reports.Materials & Method…
Automatic deep learning-based normalization of breast dynamic contrast-enhanced magnetic resonance images
Jun Zhang, Ashirbani Saha, Brian J. Soher +1
Objective: To develop an automatic image normalization algorithm for intensity correction of images from breast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) acqui…
Deep learning in radiology: an overview of the concepts and a survey of the state of the art
Maciej A. Mazurowski, Mateusz Buda, Ashirbani Saha +1
Deep learning is a branch of artificial intelligence where networks of simple interconnected units are used to extract patterns from data in order to solve complex problems. Deep l…
Deep Learning for identifying radiogenomic associations in breast cancer
Zhe Zhu, Ehab Albadawy, Ashirbani Saha +3
Purpose: To determine whether deep learning models can distinguish between breast cancer molecular subtypes based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI).…
Deep learning analysis of breast MRIs for prediction of occult invasive disease in ductal carcinoma in situ
Zhe Zhu, Michael Harowicz, Jun Zhang +4
Purpose: To determine whether deep learning-based algorithms applied to breast MR images can aid in the prediction of occult invasive disease following the di- agnosis of ductal ca…
A systematic study of the class imbalance problem in convolutional neural networks
Mateusz Buda, Atsuto Maki, Maciej A. Mazurowski
In this study, we systematically investigate the impact of class imbalance on classification performance of convolutional neural networks (CNNs) and compare frequently used methods…