3 research outputs found
A multi-stage GAN for multi-organ chest X-ray image generation and segmentation
Multi-organ segmentation of X-ray images is of fundamental importance for
computer aided diagnosis systems. However, the most advanced semantic
segmentation methods rely on deep learning and require a huge amount of labeled
images, which are rarely available due to both the high cost of human resources
and the time required for labeling. In this paper, we present a novel
multi-stage generation algorithm based on Generative Adversarial Networks
(GANs) that can produce synthetic images along with their semantic labels and
can be used for data augmentation. The main feature of the method is that,
unlike other approaches, generation occurs in several stages, which simplifies
the procedure and allows it to be used on very small datasets. The method has
been evaluated on the segmentation of chest radiographic images, showing
promising results. The multistage approach achieves state-of-the-art and, when
very few images are used to train the GANs, outperforms the corresponding
single-stage approach
Mathematical Modelling and Machine Learning Methods for Bioinformatics and Data Science Applications
Mathematical modeling is routinely used in physical and engineering sciences to help understand complex systems and optimize industrial processes. Mathematical modeling differs from Artificial Intelligence because it does not exclusively use the collected data to describe an industrial phenomenon or process, but it is based on fundamental laws of physics or engineering that lead to systems of equations able to represent all the variables that characterize the process. Conversely, Machine Learning methods require a large amount of data to find solutions, remaining detached from the problem that generated them and trying to infer the behavior of the object, material or process to be examined from observed samples. Mathematics allows us to formulate complex models with effectiveness and creativity, describing nature and physics. Together with the potential of Artificial Intelligence and data collection techniques, a new way of dealing with practical problems is possible. The insertion of the equations deriving from the physical world in the data-driven models can in fact greatly enrich the information content of the sampled data, allowing to simulate very complex phenomena, with drastically reduced calculation times. Combined approaches will constitute a breakthrough in cutting-edge applications, providing precise and reliable tools for the prediction of phenomena in biological macro/microsystems, for biotechnological applications and for medical diagnostics, particularly in the field of precision medicine