计算机科学
人工智能
分割
催交
编码(集合论)
机器学习
源代码
模式识别(心理学)
基线(sea)
监督学习
特征(语言学)
噪音(视频)
集合(抽象数据类型)
人工神经网络
图像(数学)
语言学
海洋学
哲学
系统工程
工程类
程序设计语言
地质学
操作系统
作者
Ahmed Alshenoudy,Bertram Sabrowsky-Hirsch,Stefan Thumfart,Michael Giretzlehner,Erich Kobler
出处
期刊:IFIP advances in information and communication technology
日期:2023-01-01
卷期号:: 314-325
被引量:2
标识
DOI:10.1007/978-3-031-34111-3_27
摘要
Semi-supervised learning can be a promising approach in expediting the process of annotating medical images. In this paper, we use diffusion models to learn visual representations from multi-modal medical images in an unsupervised setting. These learned representations are then employed for the challenging downstream task of brain tumor segmentation. To avoid feature selection when using pixel-level classifiers, we propose fine-tuning the noise predictor network for semantic segmentation. We compare these methods against a supervised baseline over a varying number of training samples and evaluate their performance on a substantially larger test set. Our results show that, with less than 20 training samples, all methods outperform the supervised baseline across all tumor regions. Additionally, we present a practical use-case for patient-level tumor segmentation using limited supervision. The code we used and our trained diffusion model are publicly available ( https://github.com/risc-mi/braintumor-ddpm ).
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