A Self-Training Teacher-Student Model with an Automatic Label Grader for Abdominal Skeletal Muscle Segmentation

Artificial Intelligence in Medicine(2022)CCF CSCI 2区

Univ Pittsburgh | Nanjing Med Univ

Cited 4|Views28
Abstract
Deep learning on a limited number of labels/annotations is a challenging task for medical imaging analysis. In this paper, we propose a novel self-training segmentation pipeline (Self-Seg in short) for segmenting skeletal muscle in CT images. Self-Seg starts with a small set of annotated images and then iteratively learns from un-labeled datasets to gradually improve the segmentation performance. Self-Seg follows a semi-supervised teacher -student learning scheme and there are two contributions: 1) we construct a self-attention UNet to improve segmentation over the classical UNet model, and 2) we implement an automatic label grader to implicitly incorporate medical knowledge for quality assurance of pseudo labels, from which good quality pseudo labels are identified to enhance learning of the segmentation model. We perform extensive experiments on three CT image datasets and show promising results on five evaluation settings, and we also compared our method to several baseline and related methods and achieved superior performance.
More
Translated text
Key words
Image segmentation,Semi-supervised learning,Self-attention,Teacher-student model,Skeletal muscle
求助PDF
上传PDF
Bibtex
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
  • Pretraining has recently greatly promoted the development of natural language processing (NLP)
  • We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
  • We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
  • The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
  • Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper

要点】:本文提出了一种基于自我训练的教师-学生模型及自动标签评分器的腹部骨骼肌肉分割方法,通过半监督学习提升少量标注数据的分割性能,并引入自注意力UNet和自动标签评分机制以优化模型质量和伪标签质量。

方法】:采用自训练框架,首先用少量已标注图像初始化,然后通过迭代从未标注数据中学习,不断改进分割性能;同时,使用自注意力UNet替代传统UNet,并引入自动标签评分器以利用医学知识提升伪标签的质量。

实验】:在三个CT图像数据集上进行了广泛实验,并在五种评估设置下展示了有希望的结果,同时与多种基线和相关方法比较,取得了更优性能。