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The purpose of this study is to provide the conditions for governance effectiveness and explain why the same rules often result in not the same norms.

The author proposes a “corporate governance culture” concept explaining the differences within corporate governance institutions and making it possible to measure their effectiveness. Based on a literature review that included 186 research studies published in the corporate governance field, the author found that most (160) concern structural numerical variables. Only 26 refer to behavioural and cultural issues, and they support the idea of an interdisciplinary approach to governance problems.

A significant contribution of this paper is that it proposes an integrative framework that operationalises psychological, sociological and philosophical issues that influence corporate governance mechanisms. The proposed concept can reanimate the debate about the need for tight governance regulations or leaving room for a loose governance regime.

The idea of “corporate governance culture” explains the divergences identified in studies on corporate governance mechanisms, pointing out behavioural and cultural issues as crucial aspects of governance bodies.

The cardiomegaly can be determined by the cardiothoracic ratio (CTR) which can be measured in a chest x-ray image. It is calculated based on a relationship between a size of heart and a transverse dimension of chest. The cardiomegaly is identified when the ratio is larger than a cut-off threshold. This paper aims to propose a solution to calculate the ratio for classifying the cardiomegaly in chest x-ray images.

The proposed method begins with constructing lung and heart segmentation models based on U-Net architecture using the publicly available datasets with the groundtruth of heart and lung masks. The ratio is then calculated using the sizes of segmented lung and heart areas. In addition, Progressive Growing of GANs (PGAN) is adopted here for constructing the new dataset containing chest x-ray images of three classes including male normal, female normal and cardiomegaly classes. This dataset is then used for evaluating the proposed solution. Also, the proposed solution is used to evaluate the quality of chest x-ray images generated from PGAN.

In the experiments, the trained models are applied to segment regions of heart and lung in chest x-ray images on the self-collected dataset. The calculated CTR values are compared with the values that are manually measured by human experts. The average error is 3.08%. Then, the models are also applied to segment regions of heart and lung for the CTR calculation, on the dataset computed by PGAN. Then, the cardiomegaly is determined using various attempts of different cut-off threshold values. With the standard cut-off at 0.50, the proposed method achieves 94.61% accuracy, 88.31% sensitivity and 94.20% specificity.

The proposed solution is demonstrated to be robust across unseen datasets for the segmentation, CTR calculation and cardiomegaly classification, including the dataset generated from PGAN. The cut-off value can be adjusted to be lower than 0.50 for increasing the sensitivity. For example, the sensitivity of 97.04% can be achieved at the cut-off of 0.45. However, the specificity is decreased from 94.20% to 79.78%.