计算机科学
背景(考古学)
人工智能
生成模型
磁共振弥散成像
模式识别(心理学)
代表(政治)
异常检测
生成语法
磁共振成像
医学
生物
放射科
古生物学
政治
政治学
法学
作者
Finn Behrendt,Debalina Bhattacharya,Julia Krüger,Roland Opfer,Alexander Schlaefer
出处
期刊:Cornell University - arXiv
日期:2023-03-07
标识
DOI:10.48550/arxiv.2303.03758
摘要
The use of supervised deep learning techniques to detect pathologies in brain MRI scans can be challenging due to the diversity of brain anatomy and the need for annotated data sets. An alternative approach is to use unsupervised anomaly detection, which only requires sample-level labels of healthy brains to create a reference representation. This reference representation can then be compared to unhealthy brain anatomy in a pixel-wise manner to identify abnormalities. To accomplish this, generative models are needed to create anatomically consistent MRI scans of healthy brains. While recent diffusion models have shown promise in this task, accurately generating the complex structure of the human brain remains a challenge. In this paper, we propose a method that reformulates the generation task of diffusion models as a patch-based estimation of healthy brain anatomy, using spatial context to guide and improve reconstruction. We evaluate our approach on data of tumors and multiple sclerosis lesions and demonstrate a relative improvement of 25.1% compared to existing baselines.
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