Localization of diagnostically relevant regions of interest in whole slide images

Whole slide imaging technology enables pathologists to screen biopsy images and make a diagnosis in a digital form. This creates an opportunity to understand the screening patterns of expert pathologists and extract the patterns that lead to accurate and efficient diagnoses. For this purpose, we are taking the first step to interpret the recorded actions of world-class expert pathologists on a set of digitized breast biopsy images. We propose an algorithm to extract regions of interest from the logs of image screenings using zoom levels, time and the magnitude of panning motion. Using diagnostically relevant regions marked by experts, we use the visual bag-of-words model with texture and color features to describe these regions and train probabilistic classifiers to predict similar regions of interest in new whole slide images. The proposed algorithm gives promising results for detecting diagnostically relevant regions. We hope this attempt to predict the regions that attract pathologists' attention will provide the first step in a more comprehensive study to understand the diagnostic patterns in histopathology. © 2014 IEEE

Localization of Diagnostically Relevant Regions of Interest in Whole Slide Images: a Comparative Study

Whole slide digital imaging technology enables researchers to study pathologists’ interpretive behavior as they view digital slides and gain new understanding of the diagnostic medical decision-making process. In this study, we propose a simple yet important analysis to extract diagnostically relevant regions of interest (ROIs) from tracking records using only pathologists’ actions as they viewed biopsy specimens in the whole slide digital imaging format (zooming, panning, and fixating). We use these extracted regions in a visual bag-of-words model based on color and texture features to predict diagnostically relevant ROIs on whole slide images. Using a logistic regression classifier in a cross-validation setting on 240 digital breast biopsy slides and viewport tracking logs of three expert pathologists, we produce probability maps that show 74 % overlap with the actual regions at which pathologists looked. We compare different bag-of-words models by changing dictionary size, visual word definition (patches vs. superpixels), and training data (automatically extracted ROIs vs. manually marked ROIs). This study is a first step in understanding the scanning behaviors of pathologists and the underlying reasons for diagnostic errors. © 2016, Society for Imaging Informatics in Medicine

Multi-instance multi-label learning for whole slide breast histopathology

Digitization of full biopsy slides using the whole slide imaging technology has provided new opportunities for understanding the diagnostic process of pathologists and developing more accurate computer aided diagnosis systems. However, the whole slide images also provide two new challenges to image analysis algorithms. The first one is the need for simultaneous localization and classification of malignant areas in these large images, as different parts of the image may have different levels of diagnostic relevance. The second challenge is the uncertainty regarding the correspondence between the particular image areas and the diagnostic labels typically provided by the pathologists at the slide level. In this paper, we exploit a data set that consists of recorded actions of pathologists while they were interpreting whole slide images of breast biopsies to find solutions to these challenges. First, we extract candidate regions of interest (ROI) from the logs of pathologists' image screenings based on different actions corresponding to zoom events, panning motions, and fixations. Then, we model these ROIs using color and texture features. Next, we represent each slide as a bag of instances corresponding to the collection of candidate ROIs and a set of slide-level labels extracted from the forms that the pathologists filled out according to what they saw during their screenings. Finally, we build classifiers using five different multi-instance multi-label learning algorithms, and evaluate their performances under different learning and validation scenarios involving various combinations of data from three expert pathologists. Experiments that compared the slide-level predictions of the classifiers with the reference data showed average precision values up to 62% when the training and validation data came from the same individual pathologist's viewing logs, and an average precision of 64% was obtained when the candidate ROIs and the labels from all pathologists were combined for each slide. © 2016 SPIE

Opacity calculation for target physics using the ABAKO/RAPCAL code

Radiative properties of hot dense plasmas remain a subject of current interest since they play an important role in inertial confinement fusion (ICF) research, as well as in studies on stellar physics. In particular, the understanding of ICF plasmas requires emissivities and opacities for both hydro-simulations and diagnostics. Nevertheless, the accurate calculation of these properties is still an open question and continuous efforts are being made to develop new models and numerical codes that can facilitate the evaluation of such properties. In this work the set of atomic models ABAKO/RAPCAL is presented, as well as a series of results for carbon and aluminum to show its capability for modeling the population kinetics of plasmas in both LTE and NLTE regimes. Also, the spectroscopic diagnostics of a laser-produced aluminum plasma using ABAKO/RAPCAL is discussed. Additionally, as an interesting application of these codes, fitting analytical formulas for Rosseland and Planck mean opacities for carbon plasmas are reported. These formulas are useful as input data in hydrodynamic simulation of targets where the computation task is so hard that in line computation with sophisticated opacity codes is prohibitive

A Measurement of Psi(2S) Resonance Parameters

Cross sections for e+e- to hadons, pi+pi- J/Psi, and mu+mu- have been measured in the vicinity of the Psi(2S) resonance using the BESII detector operated at the BEPC. The Psi(2S) total width; partial widths to hadrons, pi+pi- J/Psi, muons; and corresponding branching fractions have been determined to be Gamma(total)= (264+-27) keV; Gamma(hadron)= (258+-26) keV, Gamma(mu)= (2.44+-0.21) keV, and Gamma(pi+pi- J/Psi)= (85+-8.7) keV; and Br(hadron)= (97.79+-0.15)%, Br(pi+pi- J/Psi)= (32+-1.4)%, Br(mu)= (0.93+-0.08)%, respectively.Comment: 8 pages, 6 figure

Distribution of sedimentary rock types through time in a back-arc basin: A case study from the Jurassic of the Greater Caucasus (Northern Neotethys)

Abstract The evolution of sedimentary basins can be explored by analyzing the changes in their lithologies and lithofacies (i.e. predominant lithologies). The Greater Caucasus Basin, which was located at the northern margin of the Neotethys Ocean, represents a complete Sinemurian-Tithonian succession. A quantitative analysis of compiled datasets suggests that principal lithologies and lithofacies are represented by siliciclastics, shale and carbonates. The relative abundance of siliciclastics and shale decreased throughout the Jurassic, whereas that of carbonates increased. Evaporites are known from the Upper Jurassic, while volcaniclastics and volcanics, as well as coals, are known only in the Lower to Middle Jurassic. Siliceous rocks are extremely rare. Lithology and lithofacies proportions change accordingly. The Sinemurian-Bathonian sedimentary complex is siliciclastic-and-shale-dominated, whereas the Callovian-Tithonian sedimentary complex is carbonate-dominated. A major change in the character of sedimentation occurred during the Aalenian-Callovian time interval. Regional transgressions and regressions were more important controls of changes in the sedimentary rock proportions than average basin depth. Landward shoreline shifts were especially favorable for carbonate accumulation, whereas siliciclastics and shale were deposited preferentially in regressive settings. An extended area of the marine basin, its lower average depth, and a sharp bathymetric gradient favored a higher diversity of sedimentation. An orogeny at the Triassic-Jurassic transition was responsible for a large proportion of siliciclastics and extensive conglomerate deposition. An arcarc collision in the Middle Jurassic also enhanced the siliciclastic deposition. Both phases of tectonic activity were linked with an increase in volcanics and volcaniclastics. Volcanism itself might have been an important control on sedimentation. A transition to carbonate-dominated sedimentation occurred in the Late Jurassic, reflecting a tectonically calm period

Measurement of the B0-anti-B0-Oscillation Frequency with Inclusive Dilepton Events

The $B^0$-$\bar B^0$ oscillation frequency has been measured with a sample of 23 million \B\bar B pairs collected with the BABAR detector at the PEP-II asymmetric B Factory at SLAC. In this sample, we select events in which both B mesons decay semileptonically and use the charge of the leptons to identify the flavor of each B meson. A simultaneous fit to the decay time difference distributions for opposite- and same-sign dilepton events gives $\Delta m_d = 0.493 \pm 0.012{(stat)}\pm 0.009{(syst)}$ ps$^{-1}$.Comment: 7 pages, 1 figure, submitted to Physical Review Letter

Broadband Quantum Enhancement of the LIGO Detectors with Frequency-Dependent Squeezing

Quantum noise imposes a fundamental limitation on the sensitivity of interferometric gravitational-wave detectors like LIGO, manifesting as shot noise and quantum radiation pressure noise. Here, we present the first realization of frequency-dependent squeezing in full-scale gravitational-wave detectors, resulting in the reduction of both shot noise and quantum radiation pressure noise, with broadband detector enhancement from tens of hertz to several kilohertz. In the LIGO Hanford detector, squeezing reduced the detector noise amplitude by a factor of 1.6 (4.0 dB) near 1 kHz; in the Livingston detector, the noise reduction was a factor of 1.9 (5.8 dB). These improvements directly impact LIGO's scientific output for high-frequency sources (e.g., binary neutron star postmerger physics). The improved low-frequency sensitivity, which boosted the detector range by 15%-18% with respect to no squeezing, corresponds to an increase in the astrophysical detection rate of up to 65%. Frequency-dependent squeezing was enabled by the addition of a 300-meter-long filter cavity to each detector as part of the LIGO A+ upgrade