MITK-ModelFit: A generic open-source framework for model fits and their exploration in medical imaging -- design, implementation and application on the example of DCE-MRI

Many medical imaging techniques utilize fitting approaches for quantitative parameter estimation and analysis. Common examples are pharmacokinetic modeling in DCE MRI/CT, ADC calculations and IVIM modeling in diffusion-weighted MRI and Z-spectra analysis in chemical exchange saturation transfer MRI. Most available software tools are limited to a special purpose and do not allow for own developments and extensions. Furthermore, they are mostly designed as stand-alone solutions using external frameworks and thus cannot be easily incorporated natively in the analysis workflow. We present a framework for medical image fitting tasks that is included in MITK, following a rigorous open-source, well-integrated and operating system independent policy. Software engineering-wise, the local models, the fitting infrastructure and the results representation are abstracted and thus can be easily adapted to any model fitting task on image data, independent of image modality or model. Several ready-to-use libraries for model fitting and use-cases, including fit evaluation and visualization, were implemented. Their embedding into MITK allows for easy data loading, pre- and post-processing and thus a natural inclusion of model fitting into an overarching workflow. As an example, we present a comprehensive set of plug-ins for the analysis of DCE MRI data, which we validated on existing and novel digital phantoms, yielding competitive deviations between fit and ground truth. Providing a very flexible environment, our software mainly addresses developers of medical imaging software that includes model fitting algorithms and tools. Additionally, the framework is of high interest to users in the domain of perfusion MRI, as it offers feature-rich, freely available, validated tools to perform pharmacokinetic analysis on DCE MRI data, with both interactive and automatized batch processing workflows.Comment: 31 pages, 11 figures URL: http://mitk.org/wiki/MITK-ModelFi

Microfinance institutions and efficiency

Microfinance Institutions (MFIs) are special financial institutions. They have both a social nature and a for-profit nature. Their performance has been traditionally measured by means of financial ratios. The paper uses a Data Envelopment Analysis (DEA) approach to efficiency to show that ratio analysis does not capture DEA efficiency.Special care is taken in the specification of the DEA model. We take a methodological approach based on multivariate analysis. We rank DEA efficiencies under different models and specifications; e.g., particular sets of inputs and outputs. This serves to explore what is behind a DEA score. The results show that we can explain MFIs efficiency by means of four principal components of efficiency, and this way we are able to understand differences between DEA scores. It is shown that there are country effects on efficiency; and effects that depend on Non-governmental Organization (NGO)/non-NGO status of the MFI

What is the evidence of the impact of microfinance on the well-being of poor people?

The concept of microcredit was first introduced in Bangladesh by Nobel Peace Prize winner Muhammad Yunus. Professor Yunus started Grameen Bank (GB) more than 30 years ago with the aim of reducing poverty by providing small loans to the country’s rural poor (Yunus 1999). Microcredit has evolved over the years and does not only provide credit to the poor, but also now spans a myriad of other services including savings, insurance, remittances and non-financial services such as financial literacy training and skills development programmes; microcredit is now referred to as microfinance (Armendáriz de Aghion and Morduch 2005, 2010). A key feature of microfinance has been the targeting of women on the grounds that, compared to men, they perform better as clients of microfinance institutions and that their participation has more desirable development outcomes (Pitt and Khandker 1998). Despite the apparent success and popularity of microfinance, no clear evidence yet exists that microfinance programmes have positive impacts (Armendáriz de Aghion and Morduch 2005, 2010; and many others). There have been four major reviews examining impacts of microfinance (Sebstad and Chen, 1996; Gaile and Foster 1996, Goldberg 2005, Odell 2010, see also Orso 2011). These reviews concluded that, while anecdotes and other inspiring stories (such as Todd 1996) purported to show that microfinance can make a real difference in the lives of those served, rigorous quantitative evidence on the nature, magnitude and balance of microfinance impact is still scarce and inconclusive (Armendáriz de Aghion and Morduch 2005, 2010). Overall, it is widely acknowledged that no well-known study robustly shows any strong impacts of microfinance (Armendáriz de Aghion and Morduch 2005, p199-230). Because of the growth of the microfinance industry and the attention the sector has received from policy makers, donors and private investors in recent years, existing microfinance impact evaluations need to be re-investigated; the robustness of claims that microfinance successfully alleviates poverty and empowers women must be scrutinised more carefully. Hence, this review revisits the evidence of microfinance evaluations focusing on the technical challenges of conducting rigorous microfinance impact evaluations

Revisiting business relationship quality in subsistence marketplaces

Micro-entrepreneurs play a critical role in alleviating poverty in subsistence marketplaces through their business relationships with microfinance institutions. Despite the enormous importance of these relationships, a critical research question on the dimensions of business relationship quality and their overall effects on relationship outcomes remains unanswered. Thus, drawing on the relationship marketing, social exchange, and self-determination theories, this study answers the focal research question by conducting in-depth interviews (n = 30), thematic analysis, and a survey (n = 300) of micro-entrepreneurs in a subsistence marketplace. The findings show a third-order business relationship quality model with three second-order dimensions (i.e., business trust, business respect, and business reciprocity) and nine subdimensions. The findings confirm the impact of business relationship quality on business customer inspiration and business customer value examined in this marketplace. The findings also identify the mediating role of customer inspiration and both the moderating and quadratic roles of relationship quality on relationship outcomes. The predictive power of the business relationship quality model is validated through PLSpredict using a training sample (n = 270) and a holdout sample (n = 30)

Link Prediction in Complex Networks: A Survey

Link prediction in complex networks has attracted increasing attention from both physical and computer science communities. The algorithms can be used to extract missing information, identify spurious interactions, evaluate network evolving mechanisms, and so on. This article summaries recent progress about link prediction algorithms, emphasizing on the contributions from physical perspectives and approaches, such as the random-walk-based methods and the maximum likelihood methods. We also introduce three typical applications: reconstruction of networks, evaluation of network evolving mechanism and classification of partially labelled networks. Finally, we introduce some applications and outline future challenges of link prediction algorithms.Comment: 44 pages, 5 figure

Human Resources and Performance in Social Enterprises : Evidence from Microfinance Institutions

publishedVersio

An empirical analysis of national differences in the retail bank interest rates of the euro area

The availability of new harmonized data on bank interest rates allows a rigorous assessment to be made of cross-country price homogeneity/heterogeneity in euro area retail credit markets. Econometric analysis shows that the banking market is still highly segmented and that the degree of integration in a single country (Italy, taken as a benchmark for integration) is greater than in the euro area. However, national differences can be partially explained by variables reflecting the characteristics of domestic depositors and borrowers (“demand side” regressors, such as risk exposure, disposable income, alternative financing sources, average firm size) and the characteristics of the banking systems (“supply side” regressors, such as banking market concentration, asset and liability structure). The euro area prices appear different because national banking products appear different or because they are differentiated by national factors. Once these factors have been controlled for, many differences disappear.bank interest rates, convergence, integration

Eficiencia en instituciones de microfinanzas

Microfinance Institution (MFIs) are special financial institutions. The have both a social nature and a for-profit nature. Their performance has been traditionally measured by means of financial ratios. The paper uses a Data Envelopment Analysis (DEA) approach to efficiency to show that ratio analysis does not capture DEA efficiency. Special care is taken in the specification of the DEA model. We take methodological approach based on multivariate analysis. We rank DEA efficiencies under different models and specifications: e.g. particular sets of inputs and outputs. This serves to explore what is behind a DEA score. The results show that we can explain MFIs efficiency by means of four principal components of efficiency, and this way are able to understand differences between DEA scores. It is shown that there are country effects on efficiency; and effects that depend on Non-governmental Organizations (NGO)/non-NGO status of the MF

Prediction and modelling of complex social networks and their evolution.

This thesis focuses on complex social networks in the context of computational approaches for their prediction and modelling. The increasing popularity and advancement of social net- works paired with the availability of social network data enable empirical analysis, inference, prediction and modelling of social patterns. This data-driven approach towards social science is continuously evolving and is crucial for modelling and understanding of human social behaviour including predicting future social interactions for a wide range of applications. The main difference between traditional datasets and network datasets is the presence of the relational components (links) between instances (nodes) of the network. These links and nodes induce intricate local and global patterns, defining the topology of a network. The topology is ever evolving, determining the dynamics of such a networked system. The work presented in this thesis starts with an extensive analysis of three standard network models, in terms of their properties and self-interactions as well as the size and density of the resultant graphs. These crucial analysis and understanding of the main network models are utilised to later develop a comprehensive network simulation framework. A set of novel nature-inspired link prediction approaches are then developed to predict the evolution of networks, based solely on their topologies. Building on top of these approaches, enhanced topological representations of networks are subsequently combined with node characteristics for the purpose of node classification. Finally, the proposed classification methods are extensively evaluated using simulated networks from our network simulation framework as well as two real-world citation networks. The link prediction approaches proposed in this research show that the topology of the network can be further exploited to improve the prediction of future relationships. Moreover, this research demonstrates the potential of blending state-of-the-art Machine Learning techniques with graph theory. To accelerate such advancements in the field of network science, this research also offers an open- source software to provide high-quality synthetic datasets

Pathways to self-sufficiency in the microfinance ecosystem

We used latent class growth analysis to study the trajectories followed by microfinance institutions for 10 years. This technique can detect groups of firms that follow different patterns of change over time. We identified groups of institutions that followed the same strategy and iso-performance groups of institutions with the same outcome trajectory. The trajectories were analyzed with categorical regression and decision trees, which constitutes a novel approach to latent class growth analysis. Lending money to the poorest while making a profit is not straightforward and it is challenging for microfinance institutions to be self-sufficient. We found that the most useful strategy was to improve efficiency by lowering operating costs, followed by the control of credit risk. Deviating from the mission also had a positive effect on self-sufficiency, but was a strategy followed by few institutions. Rarely did changes in interest rates or not lending to women prove valuable. The findings are useful for the stakeholders of these institutions and particularly for managers