Loss function for ineffective learning reduction in End-to-End deep image segmentation network

YE Jing-wen; WU Xiao-feng

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Loss function for ineffective learning reduction in End-to-End deep image segmentation network

更新时间：2026-09-04

- Loss function for ineffective learning reduction in End-to-End deep image segmentation network
- Vol. 36, Issue 9, Pages: 38-43(2019)
- 作者机构：
  
  复旦大学信息科学与工程学院图像与智能信息处理实验室,上海,200433
- 作者简介：
- 基金信息：
- DOI：
  CLC：
- Published：2019
- 稿件说明：
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叶靖雯, 吴晓峰. 端到端深度图像分割网络中抑制无效率学习的目标损失函数设计[J]. 微电子学与计算机, 2019, 36(9): 38-43.

YE Jing-wen, WU Xiao-feng. Loss function for ineffective learning reduction in End-to-End deep image segmentation network[J]. 2019, 36(9): 38-43.
叶靖雯, 吴晓峰. 端到端深度图像分割网络中抑制无效率学习的目标损失函数设计[J]. 微电子学与计算机, 2019, 36(9): 38-43. DOI：

YE Jing-wen, WU Xiao-feng. Loss function for ineffective learning reduction in End-to-End deep image segmentation network[J]. 2019, 36(9): 38-43. DOI：

摘要

在端到端深度图像分割网络训练时

常出现前景和背景区域相差巨大的情况

造成目标特征学习不足而背景特征学习过度.对此提出一种基于代价敏感学习的目标函数构造方法:借鉴难例挖掘思想

使用表征难易程度的Focal因子对样本训练误差加权处理

有效抑制无效率学习; 仿人类视觉系统引入感受野因子

兼顾上下文信息.在医学影像数据集上对方法的有效性和可扩展性进行了测试.结果表明

新方法有助于提升网络对于小目标的检出能力

同时分割结果更贴合目标轮廓.

Abstract

In image segmentation tasks based on deep learning methods

it is common that foreground pixels occur significantly more frequently than background pixels

and consequently bias the trained network towards them. In this paper

based on cost-sensitive learning

a design method of loss function for end-to-end image segmentation network is proposed

where two improvements are provided as follows: 1) Inspired by the conception of "hard examples mining"

focal loss is introduced and extended to work for ineffective learning reduction. 2) Inspired by human visual systems

adaptive weights of receptive field are added to further consider the context information. In order to verify the validity and expansibility

the proposed method has been evaluated on severalmedicalimage datasets. The results show that the proposed method can improve the detection performance of the network for small objects

and obtain segmentation results that are more suitable for object contour.

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Keywords

references

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马海荣, 程新文.一种处理非平衡数据集的优化随机森林分类方法[J].微电子学与计算机, 2018, 35(11):28-32. DOI: 28

RODIECK R W, STONE J. Analysis of receptive fields of cat retinal ganglion cells[J]. Journal of Neurophysiology, 1965, 28(5): 833-849. DOI: 833

芮波, 张道强.脑白质损伤分割方法比较和策略改进[J].微电子学与计算机, 2011, 28(10):84-86. DOI: 84

LITJENS G, TOTH R, VAN DE VEN W, et al. Evaluation of prostate segmentation algorithms for MRI: the PROMISE12 challenge[J].Medical image analysis, 2014, 18(2): 359-373. DOI: 359

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