Using Uncertainty Information for Kidney Tumor Segmentation

被引：0
|
作者：
Michaud, Joffrey [1 ]
Arega, Tewodros Weldebirhan [1 ]
Bricq, Stephanie [1 ]
机构：
[1] Univ Bourgogne, ImViA Lab, EA 7535, Dijon, France
来源：
KIDNEY AND KIDNEY TUMOR SEGMENTATION, KITS 2023 | 2024年 / 14540卷
关键词：
Kidney semantic segmentation; 3D U-Net; Uncertainty;
D O I：
10.1007/978-3-031-54806-2_8
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Kidney cancer occurrence increases since 1990's and its main treatment is surgery. According to this, performing automatic segmentation is an important tool to develop. In this paper, we used a two stages pipeline to get the segmentation of kidney, tumor and cyst. The first stage is used to segment the kidney region to allow us to crop the data. The second stage leverages uncertainty using Monte-Carlo dropout during training by introducing an uncertainty estimate term in the loss function.
引用
收藏
页码：54 / 59
页数：6
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