| Title: |
Multi-task Learning Approach for Intracranial Hemorrhage Prognosis |
| Authors: |
Cobo, Miriam; Pérez del Barrio, Amaia; Menéndez Fernández-Miranda, Pablo; Sanz Bellón, Pablo; Lloret Iglesias, Lara; Silva, Wilson; Sub AI Technology for Life; AI Technology for Life; Xu, Xuanang; Cui, Zhiming; Sun, Kaicong; Rekik, Islem; Ouyang, Xi |
| Publication Year: |
2024 |
| Subject Terms: |
Explainable AI; Multi-task learning; Prognosis; Taverne; Theoretical Computer Science; General Computer Science |
| Description: |
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. |
| Document Type: |
book part |
| File Description: |
application/pdf |
| Language: |
English |
| ISSN: |
0302-9743 |
| Relation: |
https://dspace.library.uu.nl/handle/1874/482467 |
| Availability: |
https://dspace.library.uu.nl/handle/1874/482467 |
| Rights: |
info:eu-repo/semantics/OpenAccess |
| Accession Number: |
edsbas.6C0775D1 |
| Database: |
BASE |