blob loss: instance imbalance aware loss functions for semantic segmentation

• URL: http://arxiv.org/abs/2205.08209v3
• Date: Tue, 6 Jun 2023 17:54:34 GMT
• Title: blob loss: instance imbalance aware loss functions for semantic segmentation
• Authors: Florian Kofler, Suprosanna Shit, Ivan Ezhov, Lucas Fidon, Izabela Horvath, Rami Al-Maskari, Hongwei Li, Harsharan Bhatia, Timo Loehr, Marie Piraud, Ali Erturk, Jan Kirschke, Jan C. Peeken, Tom Vercauteren, Claus Zimmer, Benedikt Wiestler, Bjoern Menze
• Abstract summary: We propose a novel family of loss functions, emphblob loss, aimed at maximizing instance-level detection metrics. We extensively evaluate a DSC-based emphblob loss in five complex 3D semantic segmentation tasks.
• Score: 6.2334511723202
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep convolutional neural networks (CNN) have proven to be remarkably effective in semantic segmentation tasks. Most popular loss functions were introduced targeting improved volumetric scores, such as the Dice coefficient (DSC). By design, DSC can tackle class imbalance, however, it does not recognize instance imbalance within a class. As a result, a large foreground instance can dominate minor instances and still produce a satisfactory DSC. Nevertheless, detecting tiny instances is crucial for many applications, such as disease monitoring. For example, it is imperative to locate and surveil small-scale lesions in the follow-up of multiple sclerosis patients. We propose a novel family of loss functions, \emph{blob loss}, primarily aimed at maximizing instance-level detection metrics, such as F1 score and sensitivity. \emph{Blob loss} is designed for semantic segmentation problems where detecting multiple instances matters. We extensively evaluate a DSC-based \emph{blob loss} in five complex 3D semantic segmentation tasks featuring pronounced instance heterogeneity in terms of texture and morphology. Compared to soft Dice loss, we achieve 5% improvement for MS lesions, 3% improvement for liver tumor, and an average 2% improvement for microscopy segmentation tasks considering F1 score.

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