Hypoxic ischemic encephalopathy is a birth complication strongly affecting infants often resulting in death or disabilities. The underlying pathological events result from incorrect cerebral blood flow and thus complications with oxygen delivery to the brain. An automatic segmentation of hypoxic ischemic encephalopathy lesions is a crucial step in the clinical care. Therefore, to address the problem, a dedicated challenge named BONBID-HIE was organized jointly with the MICCAI 2023 conference. This work presents the contribution of the MedGIFT team to the BONBID-HIE challenge. The main idea behind the proposed method was to improve the deep network generalizability by heavy data augmentation. We show that the heavy data augmentation strongly improves the results compared to the baseline, by more than 0.2 in terms of the Dice score.