# Soft-CP: A Credible and Effective Data Augmentation for Semantic Segmentation of Medical Lesions

Source: [Soft-CP: A Credible and Effective Data Augmentation for Semantic Segmentation of Medical Lesions](<https://www.sota2.com/research/paper/68fa6c141e209131cea3203e>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T00:03:49.010Z

Authors: Pingping Dai, Licong Dong, Ruihan Zhang, Haiming Zhu, Jie Wu, Kehong Yuan

Published: 2022-03-20T09:40:04.000Z

## Abstract

The medical datasets are usually faced with the problem of scarcity and data imbalance. Moreover, annotating large datasets for semantic segmentation of medical lesions is domain-knowledge and time-consuming. In this paper, we propose a new object-blend method(short in soft-CP) that combines the Copy-Paste augmentation method for semantic segmentation of medical lesions offline, ensuring the correct edge information around the lession to solve the issue above-mentioned. We proved the method's validity with several datasets in different imaging modalities. In our experiments on the KiTS19\[2\] dataset, Soft-CP outperforms existing medical lesions synthesis approaches. The Soft-CP augementation provides gains of +26.5% DSC in the low data regime(10% of data) and +10.2% DSC in the high data regime(all of data), In offline training data, the ratio of real images to synthetic images is 3:1.

## Recorded benchmark results

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## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2203.10507>)

[PDF](<https://arxiv.org/pdf/2203.10507.pdf>)