Biophysics Informed Pathological Regularisation for Brain Tumour Segmentation

arXiv:2403.09136v1 Announce Type: cross
Abstract: Recent advancements in deep learning have significantly improved brain tumour segmentation techniques; however, the results still lack confidence and robustness as they solely consider image data without biophysical priors or pathological information. Integrating biophysics-informed regularisation is one effective way to change this situation, as it provides an prior regularisation for automated end-to-end learning. In this paper, we propose a novel approach that designs brain tumour growth Partial Differential Equation (PDE) models as a regularisation with …

abstract arxiv brain change confidence cs.cv data deep learning eess.iv however image image data information results robustness segmentation type