Multi-task Learning Approach for Intracranial Hemorrhage Prognosis

TL;DR

This paper presents a 3D multi-task learning model that improves intracranial hemorrhage prognosis accuracy and interpretability by integrating imaging, clinical, and demographic data, outperforming existing models and radiologists.

Contribution

The study introduces a novel multi-task deep learning approach that jointly predicts prognosis, Glasgow Coma Scale, and age from CT scans, enhancing performance and interpretability.

Findings

Outperforms state-of-the-art image models in ICH prognosis

Achieves superior accuracy compared to four neuroradiologists

Provides interpretable saliency maps for clinical insights

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.