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Reverse Engineering Breast MRIs: Predicting Acquisition Parameters Directly from Images
Nicholas Konz, Maciej A. Mazurowski
The image acquisition parameters (IAPs) used to create MRI scans are central to defining the appearance of the images. Deep learning models trained on data acquired using certain p…
Deep Learning for Breast MRI Style Transfer with Limited Training Data
Shixing Cao, Nicholas Konz, James Duncan +1
In this work we introduce a novel medical image style transfer method, StyleMapper, that can transfer medical scans to an unseen style with access to limited training data. This is…
Deep learning-based algorithm for assessment of knee osteoarthritis severity in radiographs matches performance of radiologists
Albert Swiecicki, Nianyi Li, Jonathan O'Donnell +5
A fully-automated deep learning algorithm matched performance of radiologists in assessment of knee osteoarthritis severity in radiographs using the Kellgren-Lawrence grading syste…
The Intrinsic Manifolds of Radiological Images and their Role in Deep Learning
Nicholas Konz, Hanxue Gu, Haoyu Dong +1
The manifold hypothesis is a core mechanism behind the success of deep learning, so understanding the intrinsic manifold structure of image data is central to studying how neural n…
Deep Learning for Classification of Thyroid Nodules on Ultrasound: Validation on an Independent Dataset
Jingxi Weng, Benjamin Wildman-Tobriner, Mateusz Buda +7
Objectives: The purpose is to apply a previously validated deep learning algorithm to a new thyroid nodule ultrasound image dataset and compare its performances with radiologists.…
Virtual vs. Reality: External Validation of COVID-19 Classifiers using XCAT Phantoms for Chest Computed Tomography
Fakrul Islam Tushar, Ehsan Abadi, Saman Sotoudeh-Paima +5
Research studies of artificial intelligence models in medical imaging have been hampered by poor generalization. This problem has been especially concerning over the last year with…
Quality or Quantity: Toward a Unified Approach for Multi-organ Segmentation in Body CT
Fakrul Islam Tushar, Husam Nujaim, Wanyi Fu +5
Organ segmentation of medical images is a key step in virtual imaging trials. However, organ segmentation datasets are limited in terms of quality (because labels cover only a few…
Knee arthritis severity measurement using deep learning: a publicly available algorithm with a multi-institutional validation showing radiologist-level performance
Hanxue Gu, Keyu Li, Roy J. Colglazier +10
The assessment of knee osteoarthritis (KOA) severity on knee X-rays is a central criteria for the use of total knee arthroplasty. However, this assessment suffers from imprecise st…
Detection of masses and architectural distortions in digital breast tomosynthesis: a publicly available dataset of 5,060 patients and a deep learning model
Mateusz Buda, Ashirbani Saha, Ruth Walsh +5
Breast cancer screening is one of the most common radiological tasks with over 39 million exams performed each year. While breast cancer screening has been one of the most studied…
Machine-Learning-Based Multiple Abnormality Prediction with Large-Scale Chest Computed Tomography Volumes
Rachel Lea Draelos, David Dov, Maciej A. Mazurowski +4
Machine learning models for radiology benefit from large-scale data sets with high quality labels for abnormalities. We curated and analyzed a chest computed tomography (CT) data s…
Normalization of breast MRIs using Cycle-Consistent Generative Adversarial Networks
Gourav Modanwal, Adithya Vellal, Maciej A. Mazurowski
Dynamic Contrast Enhanced-Magnetic Resonance Imaging (DCE-MRI) is widely used to complement ultrasound examinations and x-ray mammography during the early detection and diagnosis o…
Association of genomic subtypes of lower-grade gliomas with shape features automatically extracted by a deep learning algorithm
Mateusz Buda, Ashirbani Saha, Maciej A Mazurowski
Recent analysis identified distinct genomic subtypes of lower-grade glioma tumors which are associated with shape features. In this study, we propose a fully automatic way to quant…