Classification of semantic relations in different syntactic structures in medical text using the MeSH hierarchy

Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2005.Includes bibliographical references (leaf 38).Two different classification algorithms are evaluated in recognizing semantic relationships of different syntactic compounds. The compounds, which include noun- noun, adjective-noun, noun-adjective, noun-verb, and verb-noun, were extracted from a set of doctors' notes using a part of speech tagger and a parser. Each compound was labeled with a semantic relationship, and each word in the compound was mapped to its corresponding entry in the MeSH hierarchy. MeSH includes only medical terminology so it was extended to include everyday, non-medical terms. The two classification algorithms, neural networks and a classification tree, were trained and tested on the data set for each type of syntactic compound. Models representing different levels of MeSH were generated and fed into the neural networks. Both algorithms performed better than random guessing, and the classification tree performed better than the neural networks in predicting the semantic relationship between phrases from their syntactic structure.by Neha Bhooshan.M.Eng

The Simulation of the Movement of Fish Schools

In this paper, I explain a school behavior model, which was constructed by Aoki, Huth, and Wissel, used to describe the motion of schools of fish. Schools of fish are characterized by strong cohesion and high parallel orientation without using a leader. In this model, each fish can exhibit one of four basic behavior patterns -- repulsion, parallel orientation, attraction, and search -- based on its proximity to a neighbor fish. I modified the model in how the fish mixed the influence of its neighbors; the fish takes a weighted average of the influences of its neighbors. I constructed a computer simulation model using robots to test this model, and my data has shown that the model is quite successful in simulating the characteristics of a school of fish. The ultimate goal of this research isto apply the school behavior model to algorithms for robot formations

Advanced computer-aided diagnosis and prognosis for breast MRI

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) of the breast has become an important tool in patient work-up for breast lesions. Computer-aided diagnosis (CADx) schemes using DCE-MRI have been developed to potentially improve the accuracy with which breast lesions are diagnosed as malignant and benign. However, there are other types of MRI protocols that may generate images that are useful in differentiating malignant and benign lesions. Furthermore, clinicians are concerned not only with the differential diagnosis of the breast lesion, but also with the patients’ prognosis which is determined by considering various characteristics of malignant breast lesions. The overall objective of this research to expand the potential use of automated computerized analysis of breast MR images for clinical applications by (1) investigating multi-parametric imaging (T2-weighted MRI, high spectral and spatial (HiSS) MRI) to further improve the diagnostic accuracy of CADx and (2) extending automated computerized analysis from diagnostic classification to more complex classification with respect to prognosis. The overall hypothesis to be tested is that, by applying advanced pattern recognition and machine learning techniques, one can achieve efficient diagnostic classification on multi-parametric MR images as well as accurate prognostic classification for the purpose of generating informative prognostic DCE-MR image-based biomarkers for breast cancer. The main contributions of this research work are summarized as follows. (1) We investigated the incorporation of computerized analysis of T2-weighted MRI images in a DCE-MRI CADx scheme for distinguishing malignant and benign breast lesions. The diagnostic performance of the combination of T2-weighted, T1-weighted DCE, and geometric features was significantly higher than performances using only geometric features, only T1-weighted DCE features, and only T2-weighted features. (2) Automated computerized analysis methods were applied to precontrast HiSS MR images to characterize morphology and evaluate the diagnostic utility of HiSS features. We did not find any statistically significant difference between the performance of HiSS features to that of T1-weighted DCE features, indicating that quantitative analysis of HiSS MRI could potentially provide sufficient information to distinguish benign and malignant lesions without administration of contrast agent. (3) We explored the capability of computerized analysis to characterize histological features of malignant breast lesions including invasiveness, lymph node metastasis, and tumor grade. Specifically, we generated prognostic MRI-based biomarkers by considering three prognostic classification tasks: (i) invasive lesions vs. non-invasive lesions; (ii) lesions with positive lymph nodes vs. lesions with negative lymph nodes, and (iii) Grade 3 lesions vs. Grade 2 lesions vs. Grade 1 lesions. We found that MRI-extracted morphological and kinetic features can accurately distinguish multiple prognostic characteristics of malignant lesions. Additionally, we demonstrated that N-class Bayesian artificial neural networks (BANN) can be applied to the three-class tumor grade classification task with similar performance results to the two-class BANN; however, we did not observe any statistically significant difference. (4) We implemented and evaluated different survival prediction methods that integrated the prognostic MRI-based biomarkers (probability of being invasive, probability of being lymph node positive, and probability of being Grade 3). The prognostic image-based biomarkers showed accuracy in survival prediction across two different survival prediction methods (BANN and Cox proportional hazards model) and performed better than using the lesion features directly in survival classification, demonstrating the potential for useful survival information in the integration of the generated prognostic image-based biomarkers. The results affirm the main hypothesis of this research work. The significance of this research is that it extends automated computerized analysis of breast lesions from DCE-MRI CADx to multi-parametric MRI CADx and prognosis classification. This research provides an effective and accurate computerized scheme that has potential to help radiologists in achieving an improved characterization of breast lesions for the purposes of breast cancer diagnosis and prognosis

Cancerous Breast Lesions on Dynamic Contrast-enhanced MR Images: Computerized Characterization for Image-based Prognostic Markers

Study results show that our MR imaging computer-aided diagnosis algorithm, with use of a combination of computer-extracted MR imaging kinetic and morphologic features, has the potential to be extended to two prognostic tasks: (a) classification of noninvasive (ductal carcinoma in situ) versus invasive (invasive ductal carcinoma [IDC]) lesions and (b) further classification of IDC lesions into lesions with positive lymph nodes (LNs) and lesions with negative LNs

Open data from the first and second observing runs of Advanced LIGO and Advanced Virgo

Advanced LIGO and Advanced Virgo are monitoring the sky and collecting gravitational-wave strain data with sufficient sensitivity to detect signals routinely. In this paper we describe the data recorded by these instruments during their first and second observing runs. The main data products are gravitational-wave strain time series sampled at 16384 Hz. The datasets that include this strain measurement can be freely accessed through the Gravitational Wave Open Science Center at http://gw-openscience.org, together with data-quality information essential for the analysis of LIGO and Virgo data, documentation, tutorials, and supporting software

Search for intermediate-mass black hole binaries in the third observing run of Advanced LIGO and Advanced Virgo

International audienceIntermediate-mass black holes (IMBHs) span the approximate mass range 100−105 M⊙, between black holes (BHs) that formed by stellar collapse and the supermassive BHs at the centers of galaxies. Mergers of IMBH binaries are the most energetic gravitational-wave sources accessible by the terrestrial detector network. Searches of the first two observing runs of Advanced LIGO and Advanced Virgo did not yield any significant IMBH binary signals. In the third observing run (O3), the increased network sensitivity enabled the detection of GW190521, a signal consistent with a binary merger of mass ∼150 M⊙ providing direct evidence of IMBH formation. Here, we report on a dedicated search of O3 data for further IMBH binary mergers, combining both modeled (matched filter) and model-independent search methods. We find some marginal candidates, but none are sufficiently significant to indicate detection of further IMBH mergers. We quantify the sensitivity of the individual search methods and of the combined search using a suite of IMBH binary signals obtained via numerical relativity, including the effects of spins misaligned with the binary orbital axis, and present the resulting upper limits on astrophysical merger rates. Our most stringent limit is for equal mass and aligned spin BH binary of total mass 200 M⊙ and effective aligned spin 0.8 at 0.056 Gpc−3 yr−1 (90% confidence), a factor of 3.5 more constraining than previous LIGO-Virgo limits. We also update the estimated rate of mergers similar to GW190521 to 0.08 Gpc−3 yr−1.Key words: gravitational waves / stars: black holes / black hole physicsCorresponding author: W. Del Pozzo, e-mail: [email protected]† Deceased, August 2020