arXiv Open Access 2020

Segmentation Loss Odyssey

Jun Ma

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Abstrak

Loss functions are one of the crucial ingredients in deep learning-based medical image segmentation methods. Many loss functions have been proposed in existing literature, but are studied separately or only investigated with few other losses. In this paper, we present a systematic taxonomy to sort existing loss functions into four meaningful categories. This helps to reveal links and fundamental similarities between them. Moreover, we explore the relationship between the traditional region-based and the more recent boundary-based loss functions. The PyTorch implementations of these loss functions are publicly available at \url{https://github.com/JunMa11/SegLoss}.

Topik & Kata Kunci

eess.IV cs.CV cs.LG

Penulis (1)

Jun Ma

Format Sitasi

APA MLA BibTeX

Ma, J. (2020). Segmentation Loss Odyssey. https://arxiv.org/abs/2005.13449

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Informasi Jurnal

Tahun Terbit: 2020
Bahasa: en
Sumber Database: arXiv
Akses: Open Access ✓