Paper Info
Title
Noisy Labels are Treasure: Mean-Teacher-Assisted Confident Learning for Hepatic Vessel Segmentation
Abstract
Manually segmenting the hepatic vessels from Computer Tomography (CT) is far more expertise-demanding and laborious than other structures due to the low-contrast and complex morphology of vessels, resulting in the extreme lack of high-quality labeled data. Without sufficient high-quality annotations, the usual data-driven learning-based approaches struggle with deficient training. On the other hand, directly introducing additional data with low-quality annotations may confuse the network, leading to undesirable performance degradation. To address this issue, we propose a novel mean-teacher-assisted confident learning framework to robustly exploit the noisy labeled data for the challenging hepatic vessel segmentation task. Specifically, with the adapted confident learning assisted by a third party, i.e., the weight-averaged teacher model, the noisy labels in the additional low-quality dataset can be transformed from 'encumbrance' to 'treasure' via progressive pixel-wise soft-correction, thus providing productive guidance. Extensive experiments using two public datasets demonstrate the superiority of the proposed framework as well as the effectiveness of each component.
Year
DOI
Venue
2021
10.1007/978-3-030-87193-2_1
MEDICAL IMAGE COMPUTING AND COMPUTER ASSISTED INTERVENTION - MICCAI 2021, PT I
Keywords
DocType
Volume
Hepatic vessel, Noisy label, Confident learning
Conference
12901
ISSN
Citations 
PageRank 
0302-9743
2
0.37
References 
Authors
0
8
Name
Order
Citations
PageRank
Zhe Xu121.04
Donghuan Lu220.71
Yixin Wang31712.87
Jie Luo421.72
Jagadeesan Jayender55614.09
Kai Ma6128.91
Yefeng Zheng721.38
Li X824034.58