Multi-task Learning Approach for Intracranial Hemorrhage Prognosis
<|reference_start|>Multi-task Learning Approach for Intracranial Hemorrhage Prognosis: Prognosis after intracranial hemorrhage (ICH) is influenced by a complex interplay between imaging and tabular data. Rapid and reliable prognosis are crucial for effective patient stratification and informed treatment decision-making. In this study, we aim to enhance image-based prognosis by learning a robust feature representation shared between prognosis and the clinical and demographic variables most highly correlated with it. Our approach mimics clinical decision-making by reinforcing the model to learn valuable prognostic data embedded in the image. We propose a 3D multi-task image model to predict prognosis, Glasgow Coma Scale and age, improving accuracy and interpretability. Our method outperforms current state-of-the-art baseline image models, and demonstrates superior performance in ICH prognosis compared to four board-certified neuroradiologists using only CT scans as input. We further validate our model with interpretability saliency maps. Code is available at https://github.com/MiriamCobo/MultitaskLearning_ICH_Prognosis.git.<|reference_end|>
@article{cobo2024multi-task,
    title={Multi-task Learning Approach for Intracranial Hemorrhage Prognosis},
    author={Miriam Cobo, Amaia P'erez del Barrio, Pablo Men'endez
  Fern'andez-Miranda, Pablo Sanz Bell'on, Lara Lloret Iglesias, and Wilson
  Silva},
    journal={arXiv preprint arXiv:2408.08784},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.08784},
    primaryClass={eess.IV cs.CV}
}