{"ID":2838556,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.17146","arxiv_id":"2511.17146","title":"Learning to Look Closer: A New Instance-Wise Loss for Small Cerebral Lesion Segmentation","abstract":"Traditional loss functions in medical image segmentation, such as Dice, often under-segment small lesions because their small relative volume contributes negligibly to the overall loss. To address this, instance-wise loss functions and metrics have been proposed to evaluate segmentation quality on a per-lesion basis. We introduce CC-DiceCE, a loss function based on the CC-Metrics framework, and compare it with the existing blob loss. Both are benchmarked against a DiceCE baseline within the nnU-Net framework, which provides a robust and standardized setup. We find that CC-DiceCE loss increases detection (recall) with minimal to no degradation in segmentation performance, though with dataset-dependent trade-offs in precision. Furthermore, our multi-dataset study shows that CC-DiceCE generally outperforms blob loss.","short_abstract":"Traditional loss functions in medical image segmentation, such as Dice, often under-segment small lesions because their small relative volume contributes negligibly to the overall loss. To address this, instance-wise loss functions and metrics have been proposed to evaluate segmentation quality on a per-lesion basis. W...","url_abs":"https://arxiv.org/abs/2511.17146","url_pdf":"https://arxiv.org/pdf/2511.17146v3","authors":"[\"Luc Bouteille\",\"Alexander Jaus\",\"Jens Kleesiek\",\"Rainer Stiefelhagen\",\"Lukas Heine\"]","published":"2025-11-21T11:10:05Z","proceeding":"cs.CV","tasks":"[\"cs.CV\"]","methods":"[]","has_code":false}