In medical imaging segmentation tasks, patch sampling strategies commonly rely on positive-negative sampling, a method proven effective in frameworks such as nnU-Net and across various publicly available datasets. Existing benchmarks primarily consist of images with limited fields of view or relatively large targets. In contrast, medical image segmentation tasks involving large-field-of-view images containing numerous, variably sized lesions, as for example metastatic lesions in whole-body MRI or malignant melanoma lesions on whole-body \(^{18}\) F-FDG PET/CT, pose distinct challenges. Typically, these images contain several small metastatic lesions that contribute less than 0.01% of the total image volume. We introduce a novel data-centric instance-balanced sampling strategy tailored for these scenarios, significantly improving CPU data loading efficiency, training speed, and downstream segmentation performance. On a representative whole-body dataset it boosts the mean Dice coefficient by 4% (0.66 to 0.70), raises lesion-level sensitivity from 0.63 to 0.70, and lowers false positives per image by 23% (28.5 to 22.0), while reducing data-loading memory consumption to 4% of that required by conventional sampling. Although few open datasets yet share these large-field, lesion-dense characteristics, we anticipate a trend towards adoption of AI in oncological workflows for monitoring metastatic disease, raising the importance of our contribution.