Implementation of deep learning in infrared spectral histopathology: application to the prediction of renal allograft rejection

Spectral histopathology is a diagnostic tool based on the numerical analysis of vibrational spectral images (Raman or infrared) for which the contribution of deep learning has been superficially studied. The purpose of this thesis is to explore the potential applications of deep learning in spectral histopathology for the characterization and quantification by infrared spectral imaging of the different types of fibrosis and inflammation on renal graft biopsies. The first objective will be to study the independence of deep learning from preprocessing of infrared spectra, as suggested in literature. In a second step, the impact of the spatial definition of the acquired spectral images on the performance of convolutional neural networks will be studied. The third objective will be to compare convolutional neural networks to traditional supervised classification methods such as large support vector machines (SVM) and random forests (RF). The fourth objective of this work will be to take advantage of the capacities of autoencoders to learn transfer functions of different infrared imagers to make the proposed methodology transferable in clinical routine. The last objective of this work will be to study whether deep learning is able to identify and quantify the subtypes of fibrosis and inflammation in order to refine the diagnosis and offer a better prognosis for renal grafts as well as to guide the clinician in the choice of a therapeutic treatment adapted to each renal graft recipient.