Bi-Level Optimization to Enhance Intensity Modulated Radiation Therapy Planning

Intensity Modulated Radiation Therapy is an effective cancer treatment. Models based on the Generalized Equivalent Uniform Dose (gEUD) provide radiation plans with excellent planning target volume coverage and low radiation for organs at risk. However, manual adjustment of the parameters involved in gEUD is required to ensure that the plans meet patient-specific physical restrictions. This paper proposes a radiotherapy planning methodology based on bi-level optimization. We evaluated the proposed scheme in a real patient and compared the resulting irradiation plans with those prepared by clinical planners in hospital devices. The results in terms of efficiency and effectiveness are promising

Ensemble of classifiers based on CNN for increasing generalization ability in face image recognition

The paper considers the problem of increasing the generalization ability of classification systems by creating an ensemble of classifiers based on the CNN architecture. Different structures of the ensemble will be considered and compared. Deep learning fulfills an important role in the developed system. The numerical descriptors created in the last locally connected convolution layer of CNN flattened to the form of a vector, are subjected to a few different selection mechanisms. Each of them chooses the independent set of features, selected according to the applied assessment techniques. Their results are combined with three classifiers: softmax, support vector machine, and random forest of the decision tree. All of them do simultaneously the same classification task. Their results are integrated into the final verdict of the ensemble. Different forms of arrangement of the ensemble are considered and tested on the recognition of facial images. Two different databases are used in experiments. One was composed of 68 classes of greyscale images and the second of 276 classes of color images. The results of experiments have shown high improvement of class recognition resulting from the application of the properly designed ensemble

La simulation informatique de l’électroconvulsivothérapie (ECT) et de la stimulation magnétique transcrânienne (TMS) : ou l’explication de faible efficacité de la méthode magnétique

With help of informatics technology it is possible to simulate various physiological processes in virtual models of biological structures. In a created realistic model of the human head we made some comparative investigations over physical phenomena accompanying the electroconvulsive treatment ECT and transcranial magnetic stimulation TMS - two methods with confirmed (ECT) or presumable (TMS) antidepressant efficacy. The present investigations are a continuation of the earlier conducted study in the simple spherical model of the head. Investigations confirmed, that magnetic stimulation TMS generates a considerably weaker current flow in the brain than it is present in electroconvulsive technique. Applying of such weak stimulation in modus „at haphazard”, i.e. on the brain area which does not need to be metabolically disturbed in this patient - cannot cause an antidepressant effect at all. The results of the investigations explain not only the safety of the magnetic method, but the weak effectiveness of this method. The authors propose some methods for improvement of TMS efficacy