HyPPOCRATES · Medical Hyperspectral Image and Video Processing and Interpretation via Constrained Matrix and Tensor Factorization

Medical Hyperspectral Image and Video Processing and Interpretation via Constrained Matrix and Tensor Factorization

HyPPOCRATES project focused on the development and analysis of state-of-the-art efficient and reliable machine learning methods for processing multidimensional signals such as hyperspectral imaging data. Specifically, we emphasized the development of novel matrix and tensor decomposition methods that allow us to represent high-dimensional and large-scale signals such as hyperspectral images in such a way to capture the inherent structure of data. In doing so, we efficiently addressed several problems that show up in image processing and understanding, successfully carrying out difficult tasks such as denoising, compression, unmixing, clustering, etc. We came up with methods that process whole batches of data as well as methods amenable to handing data that are becoming available in a streaming fashion. The novelty of the proposed approach lies in the coupling of the decomposition task with model selection. This is implemented via the use of sophisticated regularization terms that are incorporated in newly formulated optimization problems. Those regularizers are carefully devised in such a way to capture the structure of the signals, thus allowing us to infer the ranks of matrices and tensors while learning the matrix/tensor factors. Nowadays, massive amounts of imaging data are generated by various sources e.g., cellphone cameras, medical imaging devices, satellite imaging sensors, etc. The development of efficient algorithmic tools that will be capable of efficient processing and extracting valuable knowledge out of these data thus becomes a pressing need. At the same time, in several domains e.g., medical imaging, the reliability of algorithms is of utmost importance. Specifically, it is crucial to come up with some guarantees regarding the performance of the algorithms that will allow us to trust their results. HyPPOCRATES addressed all those challenges by proposing computationally efficient tools that perform these tasks while elaborating on a theoretical understanding of these algorithms by shedding light on important aspects such as recovery guarantees etc. HyPPOCRATES provided algorithms that can be applied to a wide range of imaging data. The overall objective of the program was the development of a suite of machine learning algorithms that will be applied in various imaging tasks. The algorithms were built on state-of-the-art ideas of machine learning and nonconvex optimization theory and came up with theoretical guarantees as well strong empirical evidence that shows the efficiency of the derived methods. By incorporating information for the structure of imaging data such as spatial and spectral correlation which is an inherent characteristic of hyperspectral images, HyPPOCRATES algorithms can efficiently carry out tasks that are otherwise computationally expensive, requiring a significant amount of computational resources.

Текст от CORDIS, на английски · Данни: CORDIS, © Европейски съюз