95 research outputs found
Evaluation of Combined Artificial Intelligence and Radiologist Assessment to Interpret Screening Mammograms
Importance: Mammography screening currently relies on subjective human interpretation. Artificial intelligence (AI) advances could be used to increase mammography screening accuracy by reducing missed cancers and false positives. Objective: To evaluate whether AI can overcome human mammography interpretation limitations with a rigorous, unbiased evaluation of machine learning algorithms. Design, Setting, and Participants: In this diagnostic accuracy study conducted between September 2016 and November 2017, an international, crowdsourced challenge was hosted to foster AI algorithm development focused on interpreting screening mammography. More than 1100 participants comprising 126 teams from 44 countries participated. Analysis began November 18, 2016. Main Outcomes and Measurements: Algorithms used images alone (challenge 1) or combined images, previous examinations (if available), and clinical and demographic risk factor data (challenge 2) and output a score that translated to cancer yes/no within 12 months. Algorithm accuracy for breast cancer detection was evaluated using area under the curve and algorithm specificity compared with radiologists' specificity with radiologists' sensitivity set at 85.9% (United States) and 83.9% (Sweden). An ensemble method aggregating top-performing AI algorithms and radiologists' recall assessment was developed and evaluated. Results: Overall, 144 231 screening mammograms from 85 580 US women (952 cancer positive ≤12 months from screening) were used for algorithm training and validation. A second independent validation cohort included 166 578 examinations from 68 008 Swedish women (780 cancer positive). The top-performing algorithm achieved an area under the curve of 0.858 (United States) and 0.903 (Sweden) and 66.2% (United States) and 81.2% (Sweden) specificity at the radiologists' sensitivity, lower than community-practice radiologists' specificity of 90.5% (United States) and 98.5% (Sweden). Combining top-performing algorithms and US radiologist assessments resulted in a higher area under the curve of 0.942 and achieved a significantly improved specificity (92.0%) at the same sensitivity. Conclusions and Relevance: While no single AI algorithm outperformed radiologists, an ensemble of AI algorithms combined with radiologist assessment in a single-reader screening environment improved overall accuracy. This study underscores the potential of using machine learning methods for enhancing mammography screening interpretation
Azimuthal Charged-Particle Correlations and Possible Local Strong Parity Violation
Parity-odd domains, corresponding to nontrivial topological solutions of the QCD vacuum, might be created during relativistic heavy-ion collisions. These domains are predicted to lead to charge separation of quarks along the system’s orbital momentum axis. We investigate a three-particle azimuthal correlator which is a P even observable, but directly sensitive to the charge separation effect. We report measurements of charged hadrons near center-of-mass rapidity with this observable in Au+Au and Cu+Cu collisions at √sNN=200 GeV using the STAR detector. A signal consistent with several expectations from the theory is detected. We discuss possible contributions from other effects that are not related to parity violation
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The clustering of galaxies in the completed SDSS-III Baryon Oscillation Spectroscopic Survey: Cosmic flows and cosmic web from luminous red galaxies
We present a Bayesian phase-space reconstruction of the cosmic large-scale matter density and velocity fields from the Sloan Digital Sky Survey-III Baryon Oscillations Spectroscopic Survey Data Release 12 CMASS galaxy clustering catalogue. We rely on a given ∧ cold dark matter cosmology, a mesh resolution in the range of 6-10 h-1 Mpc, and a lognormal-Poisson model with a redshift-dependent non-linear bias. The bias parameters are derived from the data and a general renormalized perturbation theory approach. We use combined Gibbs and Hamiltonian sampling, implemented in the ARGO code, to iteratively reconstruct the darkmatter density field and the coherent peculiar velocities of individual galaxies, correcting hereby for coherent redshift space distortions. Our tests relying on accurate N-body-based mock galaxy catalogues show unbiased real space power spectra of the non-linear density field up to k ~ 0.2 h Mpc-1, and vanishing quadrupoles down to r ~ 20 h-1 Mpc. We also demonstrate that the non-linear cosmic web can be obtained from the tidal field tensor based on the Gaussian component of the reconstructed density field. We find that the reconstructed velocities have a statistical correlation coefficient compared to the true velocities of each individual lightcone mock galaxy of r ~ 0.68 including about 10 per cent of satellite galaxies with virial motions (about r = 0.75 without satellites). The power spectra of the velocity divergence agree well with theoretical predictions up to k ~ 0.2 h Mpc-1. This work will be especially useful to improve, for example, baryon acoustic oscillation reconstructions, kinematic Sunyaev- Zeldovich, integrated Sachs-Wolfe measurements or environmental studies
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