@inproceedings{3ea3c464b4ce46fd8dc8dd4346d9a389,
title = "Challenging Current Semi-supervised Anomaly Segmentation Methods for Brain MRI",
abstract = "In this work, we tackle the problem of Semi-Supervised Anomaly Segmentation (SAS) in Magnetic Resonance Images (MRI) of the brain, which is the task of automatically identifying pathologies in brain images. Our work challenges the effectiveness of current Machine Learning (ML) approaches in this application domain by showing that thresholding Fluid-attenuated inversion recovery (FLAIR) MR scans provides better anomaly segmentation maps than several different ML-based anomaly detection models. Specifically, our method achieves better Dice similarity coefficients and Precision-Recall curves than the competitors on various popular evaluation data sets for the segmentation of tumors and multiple sclerosis lesions. (Code available under: https://github.com/FeliMe/brain_sas_baseline",
keywords = "Anomaly detection, Brain MRI, Semi-supervised Anomaly Segmentation",
author = "Felix Meissen and Georgios Kaissis and Daniel Rueckert",
note = "Publisher Copyright: {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.; 7th International Brain Lesion Workshop, BrainLes 2021, held in conjunction with the Medical Image Computing and Computer Assisted Intervention, MICCAI 2021 ; Conference date: 27-09-2021 Through 27-09-2021",
year = "2022",
doi = "10.1007/978-3-031-08999-2_5",
language = "English",
isbn = "9783031089985",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "63--74",
editor = "Alessandro Crimi and Spyridon Bakas",
booktitle = "Brainlesion",
}