NegBio: a high-performance tool for negation and uncertainty detection in radiology reports

Negative and uncertain medical findings are frequent in radiology reports, but discriminating them from positive findings remains challenging for information extraction. Here, we propose a new algorithm, NegBio, to detect negative and uncertain findings in radiology reports. Unlike previous rule-based methods, NegBio utilizes patterns on universal dependencies to identify the scope of triggers that are indicative of negation or uncertainty. We evaluated NegBio on four datasets, including two public benchmarking corpora of radiology reports, a new radiology corpus that we annotated for this work, and a public corpus of general clinical texts. Evaluation on these datasets demonstrates that NegBio is highly accurate for detecting negative and uncertain findings and compares favorably to a widely-used state-of-the-art system NegEx (an average of 9.5% improvement in precision and 5.1% in F1-score).Comment: Final version. Accepted for publication in AMIA 2018 Informatics Summit. 9 pages, 2 figures, 4 table

DeepLesion: Automated Deep Mining, Categorization and Detection of Significant Radiology Image Findings using Large-Scale Clinical Lesion Annotations

Extracting, harvesting and building large-scale annotated radiological image datasets is a greatly important yet challenging problem. It is also the bottleneck to designing more effective data-hungry computing paradigms (e.g., deep learning) for medical image analysis. Yet, vast amounts of clinical annotations (usually associated with disease image findings and marked using arrows, lines, lesion diameters, segmentation, etc.) have been collected over several decades and stored in hospitals' Picture Archiving and Communication Systems. In this paper, we mine and harvest one major type of clinical annotation data - lesion diameters annotated on bookmarked images - to learn an effective multi-class lesion detector via unsupervised and supervised deep Convolutional Neural Networks (CNN). Our dataset is composed of 33,688 bookmarked radiology images from 10,825 studies of 4,477 unique patients. For every bookmarked image, a bounding box is created to cover the target lesion based on its measured diameters. We categorize the collection of lesions using an unsupervised deep mining scheme to generate clustered pseudo lesion labels. Next, we adopt a regional-CNN method to detect lesions of multiple categories, regardless of missing annotations (normally only one lesion is annotated, despite the presence of multiple co-existing findings). Our integrated mining, categorization and detection framework is validated with promising empirical results, as a scalable, universal or multi-purpose CAD paradigm built upon abundant retrospective medical data. Furthermore, we demonstrate that detection accuracy can be significantly improved by incorporating pseudo lesion labels (e.g., Liver lesion/tumor, Lung nodule/tumor, Abdomen lesions, Chest lymph node and others). This dataset will be made publicly available (under the open science initiative)

ChestX-ray8: Hospital-scale Chest X-ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases

The chest X-ray is one of the most commonly accessible radiological examinations for screening and diagnosis of many lung diseases. A tremendous number of X-ray imaging studies accompanied by radiological reports are accumulated and stored in many modern hospitals' Picture Archiving and Communication Systems (PACS). On the other side, it is still an open question how this type of hospital-size knowledge database containing invaluable imaging informatics (i.e., loosely labeled) can be used to facilitate the data-hungry deep learning paradigms in building truly large-scale high precision computer-aided diagnosis (CAD) systems. In this paper, we present a new chest X-ray database, namely "ChestX-ray8", which comprises 108,948 frontal-view X-ray images of 32,717 unique patients with the text-mined eight disease image labels (where each image can have multi-labels), from the associated radiological reports using natural language processing. Importantly, we demonstrate that these commonly occurring thoracic diseases can be detected and even spatially-located via a unified weakly-supervised multi-label image classification and disease localization framework, which is validated using our proposed dataset. Although the initial quantitative results are promising as reported, deep convolutional neural network based "reading chest X-rays" (i.e., recognizing and locating the common disease patterns trained with only image-level labels) remains a strenuous task for fully-automated high precision CAD systems. Data download link: https://nihcc.app.box.com/v/ChestXray-NIHCCComment: CVPR 2017 spotlight;V1: CVPR submission+supplementary; V2: Statistics and benchmark results on published ChestX-ray14 dataset are updated in Appendix B V3: Minor correction V4: new data download link upated: https://nihcc.app.box.com/v/ChestXray-NIHCC V5: Update benchmark results on the published data split in the appendi

Molecular orbital tomography beyond the plane wave approximation

The use of plane wave approximation in molecular orbital tomography via high-order harmonic generation has been questioned since it was proposed, owing to the fact that it ignores the essential property of the continuum wave function. To address this problem, we develop a theory to retrieve the valence molecular orbital directly utilizing molecular continuum wave function which takes into account the influence of the parent ion field on the continuum electrons. By transforming this wave function into momentum space, we show that the mapping from the relevant molecular orbital to the high-order harmonic spectra is still invertible. As an example, the highest orbital of $\mathrm{N_2}$ is successfully reconstructed and it shows good agreement with the \emph{ab initio} orbital. Our work clarifies the long-standing controversy and strengthens the theoretical basis of molecular orbital tomography

Probing rotational wave-packet dynamics with the structural minimum in high-order harmonic spectra

We investigate the alignment-dependent high-order harmonic spectrum generated from nonadiabatically aligned molecules around the first half rotational revival. It is found that the evolution of the molecular alignment is encoded in the structural minima. To reveal the relation between the molecular alignment and the structural minimum in the high-order harmonic spectrum, we perform an analysis based on the two-center interference model. Our analysis shows that the structural minimum position depends linearly on the inverse of the alignment parameter $$. This linear relation indicates the possibility of probing the rotational wave-packet dynamics by measuring the spectral minima

Interference of high-order harmonics generated from molecules at different alignment angles

We theoretically investigate the interference effect of high-order harmonics generated from molecules at different alignment angles. It is shown that the interference of the harmonic emissions from molecules aligned at different angles can significantly modulate the spectra and result in the anomalous harmonic cutoffs observed in a recent experiment [ Nature Phys. 7, 822 (2011) ]. The shift of the spectral minimum position with decreasing the degree of alignment is also explained by the interference effect of the harmonic emissions.Comment: 6 pages,5 figures,journa

Influence of large permanent dipoles on molecular orbital tomography

The influence of large permanent dipoles on molecular orbital tomography via high-order harmonic generation (HHG) is investigated in this work. It is found that, owing to the modification of the angle-dependent ionization rate resulting from the Stark shift, the one-side-recollision condition for the tomographic imaging can not be satisfied even with the few-cycle driving pulses. To overcome this problem, we employ a tailored driving pulse by adding a weak low-frequency pulse to the few-cycle laser pulse to control the HHG process and the recollision of the continuum electrons are effectively restricted to only one side of the core. Then we carried out the orbital reconstruction in both the length and velocity forms. The results show that, the orbital structure can only be successfully reproduced by using the dipole matrix elements projected perpendicular to the permanent dipole in both forms.Comment: 14 pages, 7 figure

Tomographic reconstruction of molecular orbitals with twofold mirror antisymmetry: overcoming the nodal plane problem

We propose a new method to overcome the nodal plane problem for the tomographic reconstruction of molecular orbitals with twofold mirror antisymmetry in the length form based on high-order harmonic generation. It is shown that, by carrying out the reconstruction procedure in the rotating laboratory frame using the component of the dipole moment parallel to the electron recollision direction, the nodal plane problem is avoided and the target orbital is successfully reconstructed. Moreover, it is found that, the proposed method can completely avoid the additional artificial lobes found in the results from the traditional method in the velocity form and therefore provides a more reliable reproduction of the target orbital.Comment: 4 figure

The selection rules of high harmonic generation: the symmetries of molecules and laser fields

The selection rules of high harmonic generation (HHG) are investigated using three-dimensional time-dependent density functional theory (TDDFT). From the harmonic spectra obtained with various real molecules and different forms of laser fields, several factors that contribute to selection rules are revealed. Extending the targets to stereoscopic molecules, it is shown that the allowed harmonics are dependent on the symmetries of the projections of molecules. For laser fields, the symmetries contributing to the selection rules are discussed according to Lissajous figures and their dynamical directivities. All the phenomena are explained by the symmetry of the full time-dependent Hamiltonian under a combined transformation. We present a systematic study on the selection rules and propose an intuitive method for the judgment of allowed harmonic orders, which can be extended to more complex molecules and various forms of laser pulses

Time-dependent population imaging for solid high harmonic generation

We propose an intuitive method, called time-dependent population imaging (TDPI), to map the dynamical processes of high harmonic generation (HHG) in solids by solving the time-dependent Schr\"{o}dinger equation (TDSE). It is shown that the real-time dynamical characteristics of HHG in solids, such as the instantaneous photon energies of emitted harmonics, can be read directly from the energy-resolved population oscillations of electrons in the TDPIs. Meanwhile, the short and long trajectories of solid HHG are illustrated clearly from TDPI. By using the TDPI, we also investigate the effects of carrier-envelope phase (CEP) in few-cycle pulses and intuitively demonstrate the HHG dynamics driven by two-color fields. Our results show that the TDPI provides a powerful tool to study the ultrafast dynamics in strong fields for various laser-solid configurations and gain an insight into HHG processes in solids