@inproceedings{d22b3c925d284430be062ff6842c6297,
title = "Expert Model Prediction Through Feature Matching",
abstract = "Supervised brain MRI segmentation performance relies on test sample alignment to the training domain. This is a function of various factors outside practical control such as imaging artefacts and demographics. One way of alleviating this risk in a automated segmentation pipeline is through a pre-segmentation domain alignment test. We explore a potential solution in the form of expert models created through clustering. We use the BraTS-2023 dataset to cluster into four groups reflecting medical consensus followed by baseline specialisation. We find that while the expert performance does not significantly outperform the baseline, the ensemble of these experts does. To scrutinise the results further we examine the performance on tumour growth segmentation of the various methods and find that the non-ensemble experts perform the best in this regard. Finally, we propose an independent performance indicator which may be used to inform aleatoric uncertainty estimation. Code available at: https://github.com/bip5/ExpertModels.",
keywords = "CNN, computational intelligence, ensemble, segmentation",
author = "Bishnu Paudel and Reyer Zwiggelaar and Otar Akanyeti",
note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.; 28th Annual Conference on Medical Image Understanding and Analysis, MIUA 2024 ; Conference date: 24-07-2024 Through 26-07-2024",
year = "2024",
doi = "10.1007/978-3-031-66958-3_19",
language = "English",
isbn = "9783031669576",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Nature",
pages = "256--269",
editor = "Yap, {Moi Hoon} and Connah Kendrick and Ardhendu Behera and Timothy Cootes and Reyer Zwiggelaar",
booktitle = "Medical Image Understanding and Analysis",
address = "Switzerland",
}