Abstract
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.
L. L. Iglesias and W. Silva—Both authors share Senior authorship.
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Acknowledgments
M. C. would like to acknowledge the support received by the Ministry of Education of Spain (FPU grant, reference FPU21-04458). The authors would like to acknowledge the support from the project AI4EOSC “Artificial Intelligence for the European Open Science Cloud” that has received funding from the European Union’s Horizon Europe research and innovation programme under grant agreement number 101058593. The authors acknowledge the neuroradiologists Marta Drake Perez, Elena Marin Diez, and David Castanedo Vazquez from Hospital Universitario Marqués de Valdecilla (Spain) for their contribution evaluating the CT scans.
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Cobo, M., Pérez del Barrio, A., Menéndez Fernández-Miranda, P., Sanz Bellón, P., Lloret Iglesias, L., Silva, W. (2025). Multi-task Learning Approach for Intracranial Hemorrhage Prognosis. In: Xu, X., Cui, Z., Rekik, I., Ouyang, X., Sun, K. (eds) Machine Learning in Medical Imaging. MLMI 2024. Lecture Notes in Computer Science, vol 15242. Springer, Cham. https://doi.org/10.1007/978-3-031-73290-4_2
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