Paper ID: 2408.08784

Multi-task Learning Approach for Intracranial Hemorrhage Prognosis

Miriam Cobo, Amaia Pérez del Barrio, Pablo Menéndez Fernández-Miranda, Pablo Sanz Bellón, Lara Lloret Iglesias, Wilson Silva

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 this https URL.

Submitted: Aug 16, 2024