Long-Term Cognitive Outcome Prediction in Stroke Patients Using Multi-task Learning on Imaging and Tabular Data
Binzer M, Hammernik K, Rueckert D, Zimmer VA (2022)
Publication Type: Conference contribution
Publication year: 2022
Journal
Publisher: Springer Science and Business Media Deutschland GmbH
Book Volume: 13564 LNCS
Pages Range: 137-148
Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Event location: Virtual, Online
ISBN: 9783031169182
DOI: 10.1007/978-3-031-16919-9_13
Abstract
While the number of stroke patients is increasing worldwide and every fifth stroke survivor is developing long-term cognitive impairment, its prediction becomes more and more important. In this work, we address the challenge of predicting any long-term cognitive impairment after a stroke using deep learning. We explore multi-task learning that combines the cognitive classification with the segmentation of brain lesions such as infarct and white matter hyperintensities or the reconstruction of the brain. Our approach is further expanded to include clinical non-imaging data to the input imaging information. The multi-task model using an autoencoder for reconstruction achieved the highest performance in classifying post-stroke cognitive impairment when only imaging data is used. The performance can be further improved by incorporating clinical information using a previously proposed dynamic affine feature map transformation. We developed and tested our approach on an in-house acquired dataset of magnetic resonance images specifically used to visualize stroke damage right after stroke occurrence. The patients were followed-up after one year to assess their cognitive status. The multi-task model trained on infarct segmentation on diffusion tensor images and enriched with clinical non-imaging information achieved the best overall performance with a balanced accuracy score of 70.3% and an area-under-the-curve of 0.791.
Involved external institutions
Germany (DE)How to cite
APA:
Binzer, M., Hammernik, K., Rueckert, D., & Zimmer, V.A. (2022). Long-Term Cognitive Outcome Prediction in Stroke Patients Using Multi-task Learning on Imaging and Tabular Data. In Islem Rekik, Ehsan Adeli, Sang Hyun Park, Celia Cintas (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 137-148). Virtual, Online: Springer Science and Business Media Deutschland GmbH.
MLA:
Binzer, Moritz, et al. "Long-Term Cognitive Outcome Prediction in Stroke Patients Using Multi-task Learning on Imaging and Tabular Data." Proceedings of the 5th International Workshop on Predictive Intelligence in Medicine, PRIME 2022, held in conjunction with 25th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2022, Virtual, Online Ed. Islem Rekik, Ehsan Adeli, Sang Hyun Park, Celia Cintas, Springer Science and Business Media Deutschland GmbH, 2022. 137-148.
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