From spoken narratives to domain knowledge: Mining linguistic data for medical image understanding

Guo, Xuan; Yu, Qi; Alm, Cecilia Ovesdotter; Calvelli, Cara; Pelz, Jeff B.; Shi, Peng; Haake, Anne R.

doi:10.1016/j.artmed.2014.08.001

Cited by 8 publications

(6 citation statements)

References 36 publications

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“…The challenges inherent in image-based diagnosis and decision-making are not unique to reproductive medicine. Efforts directed at improving accuracy and standardization of image analysis through development of computer-aided tools have recently gained attention in other medical fields [3][4][5] , including dermatology and oncology, where deep learning techniques and architectures are in current use for image analysis and to assist diagnosis 6,7 .…”

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confidence: 99%

Predicting pregnancy test results after embryo transfer by image feature extraction and analysis using machine learning

Chávez-Badiola¹,

Farías²,

Mendizabal-Ruiz

et al. 2020

Sci Rep

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Assessing the viability of a blastosyst is still empirical and non-reproducible nowadays. We developed an algorithm based on artificial vision and machine learning (and other classifiers) that predicts pregnancy using the beta human chorionic gonadotropin (b-hCG) test from both the morphology of an embryo and the age of the patients. We employed two high-quality databases with known pregnancy outcomes (n = 221). We created a system consisting of different classifiers that is feed with novel morphometric features extracted from the digital micrographs, along with other non-morphometric data to predict pregnancy. It was evaluated using five different classifiers: probabilistic bayesian, Support Vector Machines (SVM), deep neural network, decision tree, and Random Forest (RF), using a k-fold cross validation to assess the model's generalization capabilities. In the database A, the SVM classifier achieved an F1 score of 0.74, and AUC of 0.77. In the database B the RF classifier obtained a F1 score of 0.71, and AUC of 0.75. Our results suggest that the system is able to predict a positive pregnancy test from a single digital image, offering a novel approach with the advantages of using a small database, being highly adaptable to different laboratory settings, and easy integration into clinical practice.

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mentioning

confidence: 99%

Predicting pregnancy test results after embryo transfer by image feature extraction and analysis using machine learning

Chávez-Badiola¹,

Farías²,

Mendizabal-Ruiz

et al. 2020

Sci Rep

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“…For example, it is particularly useful in automatic categorization of texts or documents, news article clustering, and web sentence retrieval . In addition, word clustering is also widely used to group words in a specific professional domain; for example, biology and medicine . Our research focuses on word clustering related to the field of SE.…”

Section: Related Workmentioning

confidence: 99%

Grouping environmental factors influencing individual decision‐making behavior in software projects: A cluster analysis

Jia

Capretz

et al. 2017

J Software Evolu Process

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An individual's decision-making behavior is heavily influenced by and adapted to external environmental factors. Given that software development is a human-centered activity, individual decision-making behavior may affect the software project quality. Although environmental factors affecting decision-making behavior in software projects have been identified in prior literature, there is not yet an objective and a full taxonomy of these factors. Thus, it is not trivial to manage these complex and diverse factors. To address this deficiency, we first design a semantic similarity algorithm between words by utilizing the synonymy and hypernymy relationships in WordNet. Further, we propose a method to measure semantic similarity between phrases and apply it into k-means clustering algorithm to group these factors. Subsequently, we obtain a taxonomy of the environmental factors affecting individual decision-making behavior in software projects, which includes 11 broad categories, each containing 2 to 5 sub-categories. The taxonomy presented herein is obtained by an objective method, and quite comprehensive, with appropriate references provided. The taxonomy holds significant value for researchers and practitioners; it can help them to better understand the major aspects of environmental factors, also to predict and guide the behavior of individuals during decision making towards a successful completion of software projects. KEYWORDScluster analysis, decision-making behavior, environmental factor, semantic similarity, WordNet | INTRODUCTIONDuring software development and evolution, many decisions have to be made concerning people, processes, techniques, and tools.1 For example, facing conflicts, some developers choose a positive cooperative strategy, but others choose a negative avoidance strategy. What makes software engineers choose different decision-making behaviors? Different decision-making behaviors bring different outcomes, so the project's success depends on how individuals in a team deal with problems and make decisions. Although many tools and techniques (for example, checklists, and decision models) have been proposed to assist decision making in software projects, no one can explain how people make decisions in their contexts. 1 Individual decision-making behavior is still seldom focused on in software projects, although it is important for software development and evolution. Recently, Lenberg et al 2 proposed a concept of behavioral software engineering by taking cues from behavioral economics, which underpins the research that focuses on behavioral aspects of software engineers.According to social cognitive theory, overt behavior is influenced by intrinsic personal factors and extrinsic environmental factors. 3,4 Therefore, the factors influencing individual decision-making behavior include 2 aspects: personal and environmental factors. Personal factors, especially personality, have been explored a lot in the software engineering (SE) domain. Environmental factors are also known as situational or...

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“…Figure 3 shows a sample transcription. The medical concepts were extracted from the transcriptions using MetaMap, a medical language processing resource [17,18]. These concepts formed a highdimensional feature space, in which each image is described by the occurrences of these medical concepts.…”

Section: Paradigm Initializationmentioning

confidence: 99%

Intelligent medical image grouping through interactive learning

Guo

et al. 2016

Int J Data Sci Anal

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Image grouping in knowledge-rich domains is challenging, since domain knowledge and human expertise are key to transform image pixels into meaningful content. Manually marking and annotating images is not only laborintensive but also ineffective. Furthermore, most traditional machine learning approaches cannot bridge this gap for the absence of experts' input. We thus present an interactive machine learning paradigm that allows experts to become an integral part of the learning process. This paradigm is designed for automatically computing and quantifying interpretable grouping of dermatological images. In this way, the computational evolution of an image grouping model, its visualization, and expert interactions form a loop to improve image grouping. In our paradigm, dermatologists encode their domain knowledge about the medical images by grouping a small subset of images via a carefully designed interface. Our learning algorithm automatically incorporates these manually specified connections as constraints for reorganizing the whole image dataset. Performance evaluation shows that this paradigm effectively improves image grouping based on expert knowledge.

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From spoken narratives to domain knowledge: Mining linguistic data for medical image understanding

Cited by 8 publications

References 36 publications

Predicting pregnancy test results after embryo transfer by image feature extraction and analysis using machine learning

Predicting pregnancy test results after embryo transfer by image feature extraction and analysis using machine learning

Grouping environmental factors influencing individual decision‐making behavior in software projects: A cluster analysis

Intelligent medical image grouping through interactive learning

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