Photon counting computed tomography scanners constitute a major improvement of the field of computed tomography, opening various prospective and enabling the decomposition of computed tomography images into different materials. The material decomposition algorithm, mapping photon counts to material pathlengths, relies on a forward model with Poisson statistics. This model though suffers from noise and residual bias due to its sensitivity to calibration errors and specificities in single-pixel responses that are not captured by the material decomposition model.           This study proposes a pixel-specific and projection-based correction of the residual bias in the material decomposition estimates using artificial neural networks trained for each pixel of the detector. The neural network models were trained under supervised learning using material decomposition calibration data, scans of PE and PVC slabs of various thicknesses acquired for the calibration of the model. This method aims at the mapping of the singularities of the pixels’ responses and correct them in the projection domain. The trained models were evaluated on a set of evaluation slabs and on scans of a water phantom, in order to assess performances of homogeneity and bias correction.           The implemented solution exhibited promising results for the correction of residual bias in single pixels without impairment of the noise levels. An array of trained neural networks demonstrates its ability to correct calibration and evaluation slab data while conserving pixel-to-pixel difference. The application of the correction to the water phantom however offered nuanced results which call for further investigation of the identified issues and induced improvements of the model.