Use Case Oriented Medical Visual Information Retrieval & System Evaluation

Large amounts of medical visual data are produced daily in hospitals, while new imaging techniques continue to emerge. In addition, many images are made available continuously via publications in the scientific literature and can also be valuable for clinical routine, research and education. Information retrieval systems are useful tools to provide access to the biomedical literature and fulfil the information needs of medical professionals. The tools developed in this thesis can potentially help clinicians make decisions about difficult diagnoses via a case-based retrieval system based on a use case associated with a specific evaluation task. This system retrieves articles from the biomedical literature when querying with a case description and attached images. This thesis proposes a multimodal approach for medical case-based retrieval with focus on the integration of visual information connected to text. Furthermore, the ImageCLEFmed evaluation campaign was organised during this thesis promoting medical retrieval system evaluation

Gamification and online consumer decisions: Is the game over?

Consumption can be more than just a necessity; it can become a leisure activity. With the emergence of e-commerce and social media, products and services are just one click away; a trend that is further driven by gamified systems. This research aims to systematically analyze the most relevant academic literature on gamification, to establish if it influences online consumer decisions and, if so, which elements, mechanisms, and theories can explain it. After a thorough search from Web of Science and Scopus databases using SciMAT, 257 papers were analyzed. Twenty-nine (29) of the 36 papers found show empirical evidence that the inclusion of game elements in non-game activities has a significant influence on consumer engagement and online consumer decisions in digital contexts. Moreover, rewards and challenges were identified as the two most used mechanisms, with points, badges, and leaderboards being the most tested gamification elements. The Self- Determination Theory (SDT) and the Technology Acceptance Model (TAM) are the two most common theoretical explanations for why gamification works. Lastly, possible future studies to include thematic, methodological and theoretical agendas were discussed

Sexual dimorphism in the loud calls of Azara’s owl monkeys (Aotus azarae): evidence of sexual selection?

Primates use different types of vocalizations in a variety of contexts. Some of the most studied types have been the long distance or loud calls. These vocalizations have been associated with mate defense, mate attraction, and resource defense, and it is plausible that sexual selection has played an important role in their evolution. Focusing on identified individuals of known sex and age, we evaluated the sexual dimorphism in a type of loud calls (hoots) in a population of wild owl monkeys (Aotus azarae) in Argentina. We found evidence of sexual dimorphism in call structure, with females and males only emitting one type of call, each differing in dominant frequency and Shannon entropy. In addition, both age-related and sex-specific differences in call usage were also apparent in response to the removal of one group member. Future acoustic data will allow us to assess if there are individual characteristics and if the structure of hoot calls presents differences in relation to the social condition of owl monkeys or specific sex responses to variants of hoot calls’ traits. This will provide deeper insights into the evolution of vocal mechanisms regulating pair bonding and mate choice strategies in this and other primate species.Leakey Foundation, Wenner-Gren Foundation, National Geographic Society, NSF, National Institute on Aging, University of Pennsylvania Research Foundation, Zoological Society of San Dieg

Foot Recognition Using Deep Learning for Knee Rehabilitation

The use of foot recognition can be applied in many medical fields such as the gait pattern analysis and the knee exercises of patients in rehabilitation. Generally, a camera-based foot recognition system is intended to capture a patient image in a controlled room and background to recognize the foot in the limited views. However, this system can be inconvenient to monitor the knee exercises at home. In order to overcome these problems, this paper proposes to use the deep learning method using Convolutional Neural Networks (CNNs) for foot recognition. The results are compared with the traditional classification method using LBP and HOG features with kNN and SVM classifiers. According to the results, deep learning method provides better accuracy but with higher complexity to recognize the foot images from online databases than the traditional classification method

Generalization Performance of the Deep Learning Models in Neurodegenerative Disease Classification.

Over the past decade, machine learning gained considerable attention from the scientific community and has progressed rapidly as a result. Given its ability to detect subtle and complicated patterns, deep learning (DL) has been utilized widely in neuroimaging studies for medical data analysis and automated diagnostics with varying degrees of success. In this paper, we question the remarkable accuracies of the best performing models by assessing generalization performance of the stateof-the-art convolutional neural network (CNN) models on the classification of two most common neurodegenerative diseases, namely Alzheimer’s Disease (AD) and Parkinson’s Disease (PD) using MRI. We demonstrate the impact of the data division strategy on the model performances by comparing the results derived from two different split approaches. We first evaluated the performance of the CNN models by dividing the dataset at the subject level in which all of the MRI slices of a patient are put into either training or test set. We then observed that pooling together all slices prior to applying cross-validation, as erroneously done in a number of previous studies, leads to inflated accuracies by as much as 26% for the classification of the diseases

Overview of the ImageCLEF 2015 medical classification task

This articles describes the ImageCLEF 2015 Medical Clas-sification task. The task contains several subtasks that all use a dataset of figures from the biomedical open access literature (PubMed Cen-tral). Particularly compound figures are targeted that are frequent inthe literature. For more detailed information analysis and retrieval it isimportant to extract targeted information from the compound figures.The proposed tasks include compound figure detection (separating com-pound from other figures), multi–label classification (define all sub typespresent), figure separation (find boundaries of the subfigures) and modal-ity classification (detecting the figure type of each subfigure). The tasksare described with the participation of international research groups inthe tasks. The results of the participants are then described and analysedto identify promising techniques

Using the Developmental Indicators for the Assessment of Learning - third edition as a screener for young children: a comparison of the psychometric properties between the English and Spanish-speaking standardization samples

Demographic data show that public schools are faced with meeting the academic demands of a population that is becoming more ethnically and linguistically diverse. Preventative steps can give schools the opportunity to address the needs of its students before systemic inefficiencies can negatively impact student academic outcomes. For this reason, it is important that school psychologists remain vigilant regarding the most efficient and cost effective means to identify problems early. Since Spanish is the most prevalent language of children in the schools other than English, there is a need for school psychologists to find screening instruments that are specifically designed to convey an accurate representation of the abilities of this population. One screening instrument that has been posited as effective in assessing both English and Spanish-speakers is the Developmental Indicators for the Assessment of Learning - Third Edition (DIAL-3). The purpose of this study is to expand the work of the DIAL-3 authors to include more detailed information regarding its reliability and validity for the Spanish speaking sample. This study was conducted using the data from the standardization samples of both the English and the Spanish versions of the DIAL-3. Given the nature of the instrument, the obtained reliability estimates, computed using Cronbach's (alpha), fell within the expected range. Reliability estimate comparisons between English and Spanish-speaking samples were not statistically significant with the exception of the reliability comparisons in two domains of the DIAL-3 in the 3 years 0 months to 3 years 5 months age range. Results from additional statistical analyses conducted for this study support the discriminant validity of the test. However, a moderate linear relationship was found between the Concepts and Language Domains (r = .61, p <.01). In addition, a series of confirmatory factor analyses were conducted in order to determine the invariance of the variance-covariance matrices between the English and Spanish standardization samples. The four fit indices examined (GFI, CFI, NFI, and RMSEA) for the constrained model were within the acceptable limits. These results indicate that the three-factor model originally proposed by the test authors is adequate for both the English and Spanish versions of the DIAL-3

Medical Image Retrieval using Bag of Meaningful Visual Words: Unsupervised visual vocabulary pruning with PLSA

Content--based medical image retrieval has been proposed as a technique that allows not only for easy access to images from the relevant literature and electronic health records but also for training physicians, for research and clinical decision support. The bag-of-visual-words approach is a widely used technique that tries to shorten the semantic gap by learning meaningful features from the dataset and describing documents and images in terms of the histogram of these features. Visual vocabularies are often redundant, over--complete and noisy. Larger than required vocabularies lead to high--dimensional feature spaces, which present important disadvantages with the curse of dimensionality and computational cost being the most obvious ones. In this work a visual vocabulary pruning technique is presented. It enormously reduces the amount of required words to describe a medical image dataset with no significant effect on the accuracy. Results show that a reduction of up to 90% can be achieved without impact on the system performance. Obtaining a more compact representation of a document enables multimodal description as well as using classifiers requiring low--dimensional representations

Content–based fMRI Brain Maps Retrieval

The statistical analysis of functional magnetic resonance imaging (fMRI) is used to extract functional data of cerebral activation during a given experimental task. It allows for assessing changes in cerebral function related to cerebral activities. This methodology has been widely used and a few initiatives aim to develop shared data resources. Searching these data resources for a specific research goal remains a challenging problem. In particular, work is needed to create a global content–based (CB) fMRI retrieval capability. This work presents a CB fMRI retrieval approach based on the brain activation maps extracted using Probabilistic Independent Component Analysis (PICA). We obtained promising results on data from a variety of experiments which highlight the potential of the system as a tool that provides support for finding hidden similarities between brain activation maps

Graph Representation for Content–based fMRI Activation Map Retrieval

The use of functional magnetic resonance imaging (fMRI) to visualize brain activity in a non–invasive way is an emerging technique in neuroscience. It is expected that data sharing and the development of better search tools for the large amount of existing fMRI data may lead to a better understanding of the brain through the use of larger sample sizes or allowing collaboration among experts in various areas of expertise. In fact, there is a trend toward such sharing of fMRI data, but there is a lack of tools to effectively search fMRI data repositories, a factor which limits further research use of these repositories. Content–based (CB) fMRI brain map retrieval tools may alleviate this problem. A CB–fMRI brain map retrieval tool queries a brain activation map collection (containing brain maps showing activation areas after a stimulus is applied to a subject), and retrieves relevant brain activation maps, i.e. maps that are similar to the query brain activation map. In this work, we propose a graph–based representation for brain activation maps with the goal of improving retrieval accuracy as compared to existing methods. In this brain graph, nodes represent different specialized regions of a functional–based brain atlas. We evaluated our approach using human subject data obtained from eight experiments where a variety of stimuli were applied