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arXiv Computer Vision By Luc Bouteille, Frederic Jonske, Jens Kleesiek, Alexander Jaus

BiCC: Bidirectional Connected-Component Loss for Instance-Aware Segmentation

Read the original on arXiv Computer Vision →

The paper introduces BiCC, a bidirectional connected-component loss that pairs annotation- and prediction-derived partitions to score predicted components on their own scale. By deriving instances from predictions, BiCC directly penalizes false-positive components regardless of size, allowing a balance parameter to control the lesion-wise precision–recall trade-off. Across five datasets, BiCC outperforms existing instance-aware losses such as CC-DiceCE and blob loss in lesion-wise F1, and improves over DiceCE on multiple datasets.

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