Comparison of statistical learning approaches for cerebral aneurysm rupture assessment.

PURPOSE: Incidental aneurysms pose a challenge to physicians who need to decide whether or not to treat them. A statistical model could potentially support such treatment decisions. The aim of this study was to compare a previously developed aneurysm rupture logistic regression probability model (LRM) to other machine learning (ML) classifiers for discrimination of aneurysm rupture status. METHODS: Hemodynamic, morphological, and patient-related information of 1631 cerebral aneurysms characterized by computational fluid dynamics simulations were used to train support vector machines (SVMs) with linear and RBF kernel (RBF-SVM), k-nearest neighbors (kNN), decision tree, random forest, and multilayer perceptron (MLP) neural network classifiers for predicting the aneurysm rupture status. The classifiers' accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) were evaluated and compared to the LRM using 249 test cases obtained from two external cohorts. Additionally, important variables were determined based on the random forest and weights of the linear SVM. RESULTS: The AUCs of the MLP, LRM, linear SVM, RBF-SVM, kNN, decision tree, and random forest were 0.83, 0.82, 0.80, 0.81, 0.76, 0.70, and 0.79, respectively. The accuracy ranged between 0.76 (decision tree,) and 0.79 (linear SVM, RBF-SVM, and MLP). Important variables for predicting the aneurysm rupture status included aneurysm location, the mean surface curvature, and maximum flow velocity. CONCLUSION: The performance of the LRM was overall comparable to that of the other ML classifiers, confirming its potential for aneurysm rupture assessment. To further improve the predictions, additional information, e.g., related to the aneurysm wall, might be needed.

Evolutionary image simplification for lung nodule classification with convolutional neural networks.

PURPOSE: Understanding decisions of deep learning techniques is important. Especially in the medical field, the reasons for a decision in a classification task are as crucial as the pure classification results. In this article, we propose a new approach to compute relevant parts of a medical image. Knowing the relevant parts makes it easier to understand decisions. METHODS: In our approach, a convolutional neural network is employed to learn structures of images of lung nodules. Then, an evolutionary algorithm is applied to compute a simplified version of an unknown image based on the learned structures by the convolutional neural network. In the simplified version, irrelevant parts are removed from the original image. RESULTS: In the results, we show simplified images which allow the observer to focus on the relevant parts. In these images, more than 50% of the pixels are simplified. The simplified pixels do not change the meaning of the images based on the learned structures by the convolutional neural network. An experimental analysis shows the potential of the approach. Besides the examples of simplified images, we analyze the run time development. CONCLUSIONS: Simplified images make it easier to focus on relevant parts and to find reasons for a decision. The combination of an evolutionary algorithm employing a learned convolutional neural network is well suited for the simplification task. From a research perspective, it is interesting which areas of the images are simplified and which parts are taken as relevant.

Reaching for the cloud: on the lessons learned from grid computing technology transfer process to the biomedical community.

Natural scientists such as physicists pioneered the sharing of computing resources, which led to the creation of the Grid. The inter domain transfer process of this technology has hitherto been an intuitive process without in depth analysis. Some difficulties facing the life science community in this transfer can be understood using the Bozeman's "Effectiveness Model of Technology Transfer". Bozeman's and classical technology transfer approaches deal with technologies which have achieved certain stability. Grid and Cloud solutions are technologies, which are still in flux. We show how Grid computing creates new difficulties in the transfer process that are not considered in Bozeman's model. We show why the success of healthgrids should be measured by the qualified scientific human capital and the opportunities created, and not primarily by the market impact. We conclude with recommendations that can help improve the adoption of Grid and Cloud solutions into the biomedical community. These results give a more concise explanation of the difficulties many life science IT projects are facing in the late funding periods, and show leveraging steps that can help overcoming the "vale of tears".

On transferring the grid technology to the biomedical community.

Natural scientists such as physicists pioneered the sharing of computing resources, which resulted in the Grid. The inter domain transfer process of this technology has been an intuitive process. Some difficulties facing the life science community can be understood using the Bozeman's "Effectiveness Model of Technology Transfer". Bozeman's and classical technology transfer approaches deal with technologies that have achieved certain stability. Grid and Cloud solutions are technologies that are still in flux. We illustrate how Grid computing creates new difficulties for the technology transfer process that are not considered in Bozeman's model. We show why the success of health Grids should be measured by the qualified scientific human capital and opportunities created, and not primarily by the market impact. With two examples we show how the Grid technology transfer theory corresponds to the reality. We conclude with recommendations that can help improve the adoption of Grid solutions into the biomedical community. These results give a more concise explanation of the difficulties most life science IT projects are facing in the late funding periods, and show some leveraging steps which can help to overcome the "vale of tears".