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Physics-informed neural networks for myocardial perfusion MRI quantification
RudolfL.M.vanHerten, AmedeoChiribiri, MarcelBreeuwer....
Published date-11/25/2020
Tracer-kinetic models allow for the quantification of kinetic parameters such as blood flow from dynamic contrast-enhanced magnetic resonance (MR) images. Fitting the observed data with multi-compartment exchange models is desirable, …
Learning Curves for Drug Response Prediction in Cancer Cell Lines
AlexanderPartin, ThomasBrettin, YvonneA.Evrard....
Published date-11/25/2020
Motivated by the size of cell line drug sensitivity data, researchers have been developing machine learning (ML) models for predicting drug response to advance cancer treatment. As drug sensitivity studies …
Advancing diagnostic performance and clinical usability of neural networks via adversarial training and dual batch normalization
TianyuHan, SvenNebelung, FedericoPedersoli....
Published date-11/25/2020
DecisionMaking
Unmasking the decision-making process of machine learning models is essential for implementing diagnostic support systems in clinical practice. Here, we demonstrate that adversarially trained models can significantly enhance the usability …
The Unreasonable Effectiveness of Encoder-Decoder Networks for Retinal Vessel Segmentation
BjörnBrowatzki, Jörn-PhilippLies, ChristianWallraven....
Published date-11/25/2020
RetinalVesselSegmentation
We propose an encoder-decoder framework for the segmentation of blood vessels in retinal images that relies on the extraction of large-scale patches at multiple image-scales during training. Experiments on three …
No Subclass Left Behind: Fine-Grained Robustness in Coarse-Grained Classification Problems
NimitS.Sohoni, JaredA.Dunnmon, GeoffreyAngus....
Published date-11/25/2020
Clustering, ImageClassification
In real-world classification tasks, each class often comprises multiple finer-grained "subclasses." As the subclass labels are frequently unavailable, models trained using only the coarser-grained class labels often exhibit highly variable …
DeepShadows: Separating Low Surface Brightness Galaxies from Artifacts using Deep Learning
DimitriosTanoglidis, AleksandraĆiprijanović, AlexDrlica-Wagner....
Published date-11/24/2020
TransferLearning
Searches for low-surface-brightness galaxies (LSBGs) in galaxy surveys are plagued by the presence of a large number of artifacts (e.g., objects blended in the diffuse light from stars and galaxies, …