Prepocessing methods for nodule detection in lung CT

B-0-(B)over-bar(0) mixing and decay constants with the non-perturbatively improved action

Several quantities relevant to phenomenological studies of B-0-(B) over bar (0) mixing are computed on the lattice. Our main results are f(Bd) root(B) over cap (Bd) = 206(28) (14)(-00)(+31) MeV, xi = f(Bd) root(B) over capB(x)/f(Bd) root(B) over cap (Bd) = 1.16(7). We also obtain the related quantities f(Bs) root(B) over cap (Bs) - 237(18) (10)(-00)(+34) MeV, f(Bd) = 174(22)(-0-0-00)(+7+5+26) MeV, f(Bs) = 204(15)(-0-0-00)(+7+4+31) MeV, f(Bs)/f(Bd) = 1.17(4)(-1)(+0), f(Bd)/f(Ds) = 0.74(5). After combining our results with the experimental world average Deltam(d)((exp)), we predict Deltam(s) = 15.8(2.1)(3.3) ps(-1). We have also computed the relevant parameters for D-0-(D) over bar (0) mixing…

Machine learning classification for COVID19 patients performed on small datasets of CT scans.

In this work we evaluated the possibility of carrying out classifications of the outcome of patients with COVID19 disease through machine learning (ML) techniques working on small datasets of computed tomography (CT) images. In fact, one of the most common problems for medical artificial intelligence (AI) applications is the limited availability of annotated clinical data for model training. In the framework of the artificial intelligence in medicine (AIM) project funded by INFN, we analyzed datasets of CT scans of 79 subjects combined with clinical data containing information relating to positive outcome (no need for intensive care) or poor prognosis (admission into intensive care unit and…