Training certified detectives to track down the intrinsic shortcuts in COVID-19 chest x-ray data sets

Deep learning faces a significant challenge wherein the trained models often underperform when used with external test data sets. This issue has been attributed to spurious correlations between irrelevant features in the input data and corresponding labels. This study uses the classification of COVID-19 from chest x-ray radiographs as an example to demonstrate that the image contrast and sharpness, which are characteristics of a chest radiograph dependent on data acquisition systems and imaging parameters, can be intrinsic shortcuts that impair the model\u27s generalizability. The study proposes training certified shortcut detective models that meet a set of qualification criteria which can then identify these intrinsic shortcuts in a curated data set

Modeling inter-particle magnetic correlations in magnetite nanoparticle assemblies using x-ray magnetic scattering data

Magnetic nanoparticles are increasingly used in nanotechnologies and biomedical applications, such as drug targeting, MRI, bio-separation. Magnetite (Fe3O4) nanoparticles stand to be effective in these roles due to the non-toxic nature of magnetite and its ease of manufacture. To be more effective in these applications, a greater understanding of the magnetic behavior of a collection of magnetite nanoparticles is needed. This research seeks to discover the local magnetic ordering of ensembles of magnetite nanoparticles occurring under various external fields. To complete this study, we use x-ray resonant magnetic scattering (XRMS). Here we discuss the modeling of the magnetic scattering data using a one-dimensional chain of nanoparticles with a mix of ferromagnetic, anti-ferromagnetic, and random orders. By fitting the model to the experimental data, we extracted information about the magnetic correlations in the nanoparticle assembly

Unraveling Nanoscale Magnetic Ordering in Fe3O4 Nanoparticle Assemblies via X-rays

Understanding the correlations between magnetic nanoparticles is important for nanotechnologies, such as high-density magnetic recording and biomedical applications, where functionalized magnetic particles are used as contrast agents and for drug delivery. The ability to control the magnetic state of individual particles depends on the good knowledge of the magnetic correlations between particles when assembled. Inaccessible via standard magnetometry techniques, nanoscale magnetic ordering in self-assemblies of Fe3O4 nanoparticles is here unveiled via X-ray resonant magnetic scattering (XRMS). Measured throughout the magnetization process, the XRMS signal reveals size-dependent inter-particle magnetic correlations. Smaller (5 nm) particles show little magnetic correlations, even when packed close together, yielding to magnetic disorder in the absence of an external field, i.e., superparamagnetism. In contrast, larger (11 nm) particles tend to be more strongly correlated, yielding a mix of magnetic orders including ferromagnetic and anti-ferromagnetic orders. These magnetic correlations are present even when the particles are sparsely distributed