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An Experimental Study of Data Heterogeneity in Federated Learning Methods for Medical Imaging
Liangqiong Qu, Niranjan Balachandar, Daniel L Rubin
Federated learning enables multiple institutions to collaboratively train machine learning models on their local data in a privacy-preserving way. However, its distributed nature o…
COVID-19 Lung Lesion Segmentation Using a Sparsely Supervised Mask R-CNN on Chest X-rays Automatically Computed from Volumetric CTs
Vignav Ramesh, Blaine Rister, Daniel L. Rubin
Chest X-rays of coronavirus disease 2019 (COVID-19) patients are frequently obtained to determine the extent of lung disease and are a valuable source of data for creating artifici…
Beam dynamics corrections to the Run-1 measurement of the muon anomalous magnetic moment at Fermilab
T. Albahri, A. Anastasi, K. Badgley +174
This paper presents the beam dynamics systematic corrections and their uncertainties for the Run-1 data set of the Fermilab Muon g-2 Experiment. Two corrections to the measured muo…
Measurement of the Positive Muon Anomalous Magnetic Moment to 0.46 ppm
B. Abi, T. Albahri, S. Al-Kilani +234
We present the first results of the Fermilab Muon g-2 Experiment for the positive muon magnetic anomaly . The anomaly is determined from the precision measurem…
Addressing catastrophic forgetting for medical domain expansion
Sharut Gupta, Praveer Singh, Ken Chang +13
Model brittleness is a key concern when deploying deep learning models in real-world medical settings. A model that has high performance at one institution may suffer a significant…
Learning domain-agnostic visual representation for computational pathology using medically-irrelevant style transfer augmentation
Rikiya Yamashita, Jin Long, Snikitha Banda +2
Suboptimal generalization of machine learning models on unseen data is a key challenge which hampers the clinical applicability of such models to medical imaging. Although various…