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Xu, Songhua

Publications and source records attributed to Xu, Songhua.

At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Predicting Lung Cancer Incidence from Air Pollution Exposures Using Shapelet-based Time Series Analysis

In this paper we investigated whether the geographical variation of lung cancer incidence can be predicted through examining the spatiotemporal trend of particulate matter air pollution levels. Regional trends of air pollution levels were analyzed by a novel shapelet-based time series analysis technique. First, we identified U.S. counties with reportedly high and low lung cancer incidence between 2008 and 2012 via the State Cancer Profiles provided by the National Cancer Institute. Then, we collected particulate matter exposure levels (PM2.5 and PM10) of the counties for the previous decade (1998-2007) via the AirData dataset provided by the Environmental Protection Agency. Using shapelet-based time series pattern mining, regional environmental exposure profiles were examined to identify frequently occurring sequential exposure patterns. Finally, a binary classifier was designed to predict whether a U.S. region is expected to experience high lung cancer incidence based on the region’s PM2.5 and PM10 exposure the decade prior. The study confirmed the association between prolonged PM exposure and lung cancer risk. In addition, the study findings suggest that not only cumulative exposure levels but also the temporal variability of PM exposure influence lung cancer risk.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2015

Personalized professional content recommendation

A personalized content recommendation system includes a client interface configured to automatically monitor a user's information data stream transmitted on the Internet. A hybrid contextual behavioral and collaborative personal interest inference engine resident to a non-transient media generates automatic predictions about the interests of individual users of the system. A database server retains the user's personal interest profile based on a plurality of monitored information. The system also includes a server programmed to filter items in an incoming information stream with the personal interest profile and is further programmed to identify only those items of the incoming information stream that substantially match the personal interest profile.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2015

Recommending personally interested contents by text mining, filtering, and interfaces

A personalized content recommendation system includes a client interface device configured to monitor a user's information data stream. A collaborative filter remote from the client interface device generates automated predictions about the interests of the user. A database server stores personal behavioral profiles and user's preferences based on a plurality of monitored past behaviors and an output of the collaborative user personal interest inference engine. A programmed personal content recommendation server filters items in an incoming information stream with the personal behavioral profile and identifies only those items of the incoming information stream that substantially matches the personal behavioral profile. The identified personally relevant content is then recommended to the user following some priority that may consider the similarity between the personal interest matches, the context of the user information consumption behaviors that may be shown by the user's content consumption mode.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2015

Residential Mobility and Lung Cancer Risk: Data-Driven Exploration Using Internet Sources

Frequent relocation has been linked to health decline, particularly with respect to emotional and psychological wellbeing. In this paper we investigate whether there is an association between frequent relocation and lung cancer risk. For the initial investigation we leverage two online data sources to collect cancer and control subjects using web crawling and tailored text mining. The two data sources share different strengths and weaknesses in terms of the amount of detail, population representation, and sample size. One data source includes online obituaries. The second data source includes augmented LinkedIn profiles. For each data source, the subjects spatiotemporal history is reconstructed from the available information provided in the obituaries and from the education and work experience provided in the LinkedIn profiles. The study shows that lung cancer subjects have higher mobility frequency than the control group. This trend is consistent for both data sources.

Yoon, Hong-Jun↗
DOE OSTI2013

High-Efficacy Modeling of Human Perceptual Similarity Opinions

The computer code relates to human nature studies and more specifically to a novel graphical user interface (GUI) that facilitates high-efficacy collection of perceptual similarity opinions of a user in an effective and intuitive manner. The GUI presents a base image for rating its similarity to seven peripheral images that are displayed simultaneously following a circular layout. The user is asked to report the base image's pairwise similarity to each peripheral image on a fixed scale while preserving the relative ranking among all peripheral images. The collected data are then used to predict the user's subjective opinions regarding the perceptual similarity of images.

Xu, Songhua↗
DOE OSTI2013

Comparative analysis of data collection methods for individualized modeling of radiologists' visual similarity judgments

Rationale and Objectives: We conducted an observer study to investigate how the data collection method affects the efficacy of modeling individual radiologists judgments regarding the perceptual similarity of breast masses on mammograms. Materials and Methods: Institutional review board approval was obtained prior to the study. Six observers of variable experience levels in breast imaging were recruited to assess the perceptual similarity of mammographic masses. The observers subjective judgments were collected using: (i) a rating method, (ii) a preference method, and (iii) a hybrid method combining rating and ranking. Personalized user models were developed with the collected data to predict observers opinions. The relative efficacy of each data collection method was assessed based on the classification accuracy of the resulting user models. Results: The hybrid data collection method produced significantly more accurate individualized user models of perceptual opinions with comparable and sometimes better time efficiency than the other two data collection methods. The user models derived from hybrid data were clearly superior even when developed with a dramatically smaller number of training cases. Conclusions: A hybrid method combining rating and ranking is an intuitive and efficient way for collecting subjective similarity judgments to model human perceptual opinions with a higher accuracy than other more commonly used data collection methods.

breast imaging↗
DOE OSTI2013

Investigating the Association of Eye Gaze Pattern and Diagnostic Error in Mammography

The objective of this study was to investigate the association between eye-gaze patterns and the diagnostic accuracy of radiologists for the task of assessing the likelihood of malignancy of mammographic masses. Six radiologists (2 expert breast imagers and 4 Radiology residents of variable training) assessed the likelihood of malignancy of 40 biopsy-proven mammographic masses (20 malignant and 20 benign) on a computer monitor. Eye-gaze data were collected using a commercial remote eye-tracker. Upon reviewing each mass, the radiologists were also asked to provide their assessment regarding the probability of malignancy of the depicted mass as well as a rating regarding the perceived difficulty of the diagnostic task. The collected data were analyzed using established algorithms and various quantitative metrics were extracted to characterize the recorded gaze patterns. The extracted metrics were correlated with the radiologists diagnostic decisions and perceived complexity scores. Results showed that the visual gaze pattern of radiologists varies substantially, not only depending on their experience level but also among individuals. However, some eye gaze metrics appear to correlate with diagnostic error and perceived complexity more consistently. These results suggest that although gaze patterns are generally associated with diagnostic error and the human perceived difficulty of the diagnostic task, there are substantially individual differences that are not explained simply by the experience level of the individual performing the diagnostic task.

Voisin, Sophie↗
DOE OSTI2013

A Novel Graphical User Interface for High-Efficacy Modeling of Human Perceptual Similarity Opinions

We present a novel graphical user interface (GUI) that facilitates high-efficacy collection of perceptual similarity opinions of a user in an effective and intuitive manner. The GUI is based on a hybrid mechanism that combines ranking and rating. Namely, it presents a base image for rating its similarity to seven peripheral images that are displayed simultaneously following a circular layout. The user is asked to report the base image s pairwise similarity to each peripheral image on a fixed scale while preserving the relative ranking among all peripheral images. The collected data are then used to predict the user s subjective opinions regarding the perceptual similarity of images. We tested this new approach against two methods commonly used in perceptual similarity studies: (1) a ranking method that presents triplets of images for selecting the image pair with the highest internal similarity and (2) a rating method that presents pairs of images for rating their relative similarity on a fixed scale. We aimed to determine which data collection method was the most time efficient and effective for predicting a user s perceptual opinions regarding the similarity of mammographic masses. Our study was conducted with eight individuals. By using the proposed GUI, we were able to derive individual user profiles that were 41.4% to 46.9% more accurate than those derived with the other two data collection GUIs. The accuracy improvement was statistically significant.

Kress, James M↗
DOE OSTI2012

A Novel Local Learning based Approach With Application to Breast Cancer Diagnosis

The purpose of this study is to develop and evaluate a novel local learning-based approach for computer-assisted diagnosis of breast cancer. Our new local learning based algorithm using the linear logistic regression method as its base learner is described. Overall, our algorithm will perform its stochastic searching process until the total allowed computing time is used up by our random walk process in identifying the most suitable population subdivision scheme and their corresponding individual base learners. The proposed local learning-based approach was applied for the prediction of breast cancer given 11 mammographic and clinical findings reported by physicians using the BI-RADS lexicon. Our database consisted of 850 patients with biopsy confirmed diagnosis (290 malignant and 560 benign). We also compared the performance of our method with a collection of publicly available state-of-the-art machine learning methods. Predictive performance for all classifiers was evaluated using 10-fold cross validation and Receiver Operating Characteristics (ROC) analysis. Figure 1 reports the performance of 54 machine learning methods implemented in the machine learning toolkit Weka (version 3.0). We introduced a novel local learning-based classifier and compared it with an extensive list of other classifiers for the problem of breast cancer diagnosis. Our experiments show that the algorithm superior prediction performance outperforming a wide range of other well established machine learning techniques. Our conclusion complements the existing understanding in the machine learning field that local learning may capture complicated, non-linear relationships exhibited by real-world datasets.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2012

Predictive Modeling of Human Perception Subjectivity: Feasibility Study of Mammographic Lesion Similarity

The majority of clinical content-based image retrieval (CBIR) studies disregard human perception subjectivity, aiming to duplicate the consensus expert assessment of the visual similarity on example cases. The purpose of our study is twofold: (i) discern better the extent of human perception subjectivity when assessing the visual similarity of two images with similar semantic content, and (ii) explore the feasibility of personalized predictive modeling of visual similarity. We conducted a human observer study in which five observers of various expertise were shown ninety-nine triplets of mammographic masses with similar BI-RADS descriptors and were asked to select the two masses with the highest visual relevance. Pairwise agreement ranged between poor and fair among the five observers, as assessed by the kappa statistic. The observers' self-consistency rate was remarkably low, based on repeated questions where either the orientation or the presentation order of a mass was changed. Various machine learning algorithms were explored to determine whether they can predict each observer's personalized selection using textural features. Many algorithms performed with accuracy that exceeded each observer's self-consistency rate, as determined using a cross-validation scheme. This accuracy was statistically significantly higher than would be expected by chance alone (two-tailed p-value ranged between 0.001 and 0.01 for all five personalized models). The study confirmed that human perception subjectivity should be taken into account when developing CBIR-based medical applications.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2012

Retrieving Biomedical Images through Content-Based Learning from Examples Using Fine Granularity

Traditional content-based image retrieval methods based on learning from examples analyze and attempt to understand high-level semantics of an image as a whole. They typically apply certain case-based reasoning technique to interpret and retrieve images through measuring the semantic similarity or relatedness between example images and search candidate images. The drawback of such a traditional content-based image retrieval paradigm is that the summation of imagery contents in an image tends to lead to tremendous variation from image to image. Hence, semantically related images may only exhibit a small pocket of common elements, if at all. Such variability in image visual composition poses great challenges to content-based image retrieval methods that operate at the granularity of entire images. In this study, we explore a new content-based image retrieval algorithm that mines visual patterns of finer granularities inside a whole image to identify visual instances which can more reliably and generically represent a given search concept. We performed preliminary experiments to validate our new idea for content-based image retrieval and obtained very encouraging results.

59 BASIC BIOLOGICAL SCIENCES↗