Extracting Patient Demographics and Personal Medical Information from Online Health Forums
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Liu, Yang↗
Engineering topics
Tourassi, Georgia
Publications and source records attributed to Tourassi, Georgia.
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↗
A User-Oriented Web Crawler for Selectively Acquiring Online Content in E-Health Research
A smart web crawler that can intelligently acquire social media content on the Internet to meet the specific online data source acquisition needs of cancer researchers.
Xu, Songhua↗
Predicting diagnostic error in radiology via eye-tracking and image analytics: Preliminary investigation in mammography
Purpose: The primary aim of the present study was to test the feasibility of predicting diagnostic errors in mammography by merging radiologists’ gaze behavior and image characteristics. A secondary aim was to investigate group-based and personalized predictive models for radiologists of variable experience levels.Methods: The study was performed for the clinical task of assessing the likelihood of malignancy of mammographic masses. Eye-tracking data and diagnostic decisions for 40 cases were acquired from four Radiology residents and two breast imaging experts as part of an IRB-approved pilot study. Gaze behavior features were extracted from the eye-tracking data. Computer-generated and BIRADS images features were extracted from the images. Finally, machine learning algorithms were used to merge gaze and image features for predicting human error. Feature selection was thoroughly explored to determine the relative contribution of the various features. Group-based and personalized user modeling was also investigated.Results: Machine learning can be used to predict diagnostic error by merging gaze behavior characteristics from the radiologist and textural characteristics from the image under review. Leveraging data collected from multiple readers produced a reasonable group model [area under the ROC curve (AUC) = 0.792 ± 0.030]. Personalized user modeling was far more accurate for the more experienced readers (AUC = 0.837 ± 0.029) than for the less experienced ones (AUC = 0.667 ± 0.099). The best performing group-based and personalized predictive models involved combinations of both gaze and image features.Conclusions: Diagnostic errors in mammography can be predicted to a good extent by leveraging the radiologists’ gaze behavior and image content.
62 RADIOLOGY AND NUCLEAR MEDICINE↗
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↗
Evaluation of computer-aided detection and diagnosis systems
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Petrick, Nicholas↗
Investigating the Link Between Radiologists Gaze, Diagnostic Decision, and Image Content
Objective: To investigate machine learning for linking image content, human perception, cognition, and error in the diagnostic interpretation of mammograms. Methods: Gaze data and diagnostic decisions were collected from six radiologists who reviewed 20 screening mammograms while wearing a head-mounted eye-tracker. Texture analysis was performed in mammographic regions that attracted radiologists attention and in all abnormal regions. Machine learning algorithms were investigated to develop predictive models that link: (i) image content with gaze, (ii) image content and gaze with cognition, and (iii) image content, gaze, and cognition with diagnostic error. Both group-based and individualized models were explored. Results: By pooling the data from all radiologists machine learning produced highly accurate predictive models linking image content, gaze, cognition, and error. Merging radiologists gaze metrics and cognitive opinions with computer-extracted image features identified 59% of the radiologists diagnostic errors while confirming 96.2% of their correct diagnoses. The radiologists individual errors could be adequately predicted by modeling the behavior of their peers. However, personalized tuning appears to be beneficial in many cases to capture more accurately individual behavior. Conclusions: Machine learning algorithms combining image features with radiologists gaze data and diagnostic decisions can be effectively developed to recognize cognitive and perceptual errors associated with the diagnostic interpretation of mammograms.
Tourassi, Georgia↗
Transforming Epidemiology for 21st Century Medicine and Public Health
n 2012, the National Cancer Institute (NCI) engaged the scientific community to provide a vision for cancer epidemiology in the 21st century. Eight overarching thematic recommendations, with proposed corresponding actions for consideration by funding agencies, professional societies, and the research community emerged from the collective intellectual discourse. The themes are (i) extending the reach of epidemiology beyond discovery and etiologic research to include multilevel analysis, intervention evaluation, implementation, and outcomes research; (ii) transforming the practice of epidemiology by moving toward more access and sharing of protocols, data, metadata, and specimens to foster collaboration, to ensure reproducibility and replication, and accelerate translation; (iii) expanding cohort studies to collect exposure, clinical, and other information across the life course and examining multiple health-related endpoints; (iv) developing and validating reliable methods and technologies to quantify exposures and outcomes on a massive scale, and to assess concomitantly the role of multiple factors in complex diseases; (v) integrating big data science into the practice of epidemiology; (vi) expanding knowledge integration to drive research, policy, and practice; (vii) transforming training of 21st century epidemiologists to address interdisciplinary and translational research; and (viii) optimizing the use of resources and infrastructure for epidemiologic studies. These recommendations can transform cancer epidemiology and the field of epidemiology, in general, by enhancing transparency, interdisciplinary collaboration, and strategic applications of new technologies. They should lay a strong scientific foundation for accelerated translation of scientific discoveries into individual and population health benefits.
Khoury, Muin J↗
Automated assessment of bilateral breast volume asymmetry as a breast cancer biomarker during mammographic screening
The biological concept of bilateral symmetry as a marker of developmental stability and good health is well established. Although most individuals deviate slightly from perfect symmetry, humans are essentially considered bilaterally symmetrical. Consequently, increased fluctuating asymmetry of paired structures could be an indicator of disease. There are several published studies linking bilateral breast size asymmetry with increased breast cancer risk. These studies were based on radiologists manual measurements of breast size from mammographic images. We aim to develop a computerized technique to assess fluctuating breast volume asymmetry in screening mammograms and investigate whether it correlates with the presence of breast cancer. Using a large database of screening mammograms with known ground truth we applied automated breast region segmentation and automated breast size measurements in CC and MLO views using three well established methods. All three methods confirmed that indeed patients with breast cancer have statistically significantly higher fluctuating asymmetry of their breast volumes. However, statistically significant difference between patients with cancer and benign lesions was observed only for the MLO views. The study suggests that automated assessment of global bilateral asymmetry could serve as a breast cancer risk biomarker for women undergoing mammographic screening. Such biomarker could be used to alert radiologists or computer-assisted detection (CAD) systems to exercise increased vigilance if higher than normal cancer risk is suspected.
Williams, Alex C↗
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↗
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↗
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↗
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↗