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Phase-contrast imaging is an imaging technique, which provides an advantage over the traditional x-ray imaging by improving the contours of the tissues, thus providing higher contrast between tissues. To advance in this filed, the researchers from the Laboratory of Computer Simulations in Medicine are building a custom optical setup to generate diffraction pattern corresponding to the phase contrast data, but in the optical range. This paper reports on the current state of the optical system prototype and its initial validation and use.

In the present study we evaluate the performance of various training schemes for the locally recurrent probabilistic neural network and seek for advantageous tradeoffs between required training time and classification accuracy. Specifically, we consider training schemes which make use of a simple incremental procedure for adjusting sigma, as well as methods based on particle swarm optimization or differential evolution in different configurations. The experimental evaluation was carried out in common experimental protocol based on the Parkinson speech dataset. The experimental results show that with a proper training configuration a high accuracy can be achieved even with limited training data.

The development of new techniques for detection of breast cancer often relies on virtual clinical trials. These trials however require vast number of x-ray images of the breast containing realistically looking pathologies. For this purpose, as many as possible and as realistic as possible physical models of breast lesions are needed. The goal of this study is to create a methodology for manufacturing various breast abnormalities, with a detail description of the stages needed for their development. These models are to be used for development of new breast cancer detection technologies.