Marc Sauget

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This paper presents an incremental learning algorithm for feed-forward neural networks used as approximators of real world data. This algorithm allows neural networks of limited size to be obtained, providing better performances. The algorithm is compared to two of the main incremental algorithms (Dunkin and cascade correlation) in the respective contexts(More)
The purpose of this work is to further study the relevance of accelerating the Monte Carlo calculations for the gamma rays external radiotherapy through feed-forward neural networks. We have previously presented a parallel incremental algorithm that builds neural networks of reduced size, while providing high quality approximations of the dose deposit. Our(More)
This paper presents a parallel and fault tolerant version of an incremental learning algorithm for feed-forward neural networks used as function approximators. It has been shown in previous works that our incremental algorithm builds networks of reduced size while providing high quality approximations for real data sets. However, for very large sets, the(More)
One of the possibilities to enhance the accuracy of lung radiotherapy is to improve the understanding of the individual lung motion of each patient. Indeed, using this knowledge, it becomes possible to follow the evolution of the clinical target volume defined by a set of points according to the lung breathing phase. This paper presents an innovative method(More)
In the case of a radiological emergency situation, involving accidental human exposure, it is necessary to establish as soon as possible a dosimetry evaluation. In most cases, this evaluation is based on numerical representations and models of the victims. Unfortunately, personalised and realistic human representations are often unavailable for the exposed(More)
In case of a radiological emergency situation involving accidental human exposure, a dosimetry evaluation must be established as soon as possible. In most cases, this evaluation is based on numerical representations and models of victims. Unfortunately, personalised and realistic human representations are often unavailable for the exposed subjects. However,(More)
To optimize the delivery in lung radiation therapy, a better understanding of the tumor motion is required. On the one hand to have a better tumor-targeting efficiency, and on the other hand to avoid as much as possible normal tissues. The 4D-CT allows to quantify tumor motion, but due to artifacts it introduces biases and errors in tumor localization.(More)
PURPOSE A way to improve the accuracy of lung radiotherapy for a patient is to get a better understanding of its lung motion. Indeed, thanks to this knowledge it becomes possible to follow the displacements of the clinical target volume (CTV) induced by the lung breathing. This paper presents a feasibility study of an original method to simulate the(More)
The purpose of this work is to further study the relevance of accelerating the Monte-Carlo calculations for the gamma rays external radiotherapy through feed-forward neural networks. We have previously presented a parallel incremental algorithm that builds neural networks of reduced size, while providing high quality approximations of the dose deposit [4].(More)