TY - GEN
T1 - Self-Propagative Multi-Task Learning for Predicting Cardiometabolic Risk Factors
AU - Ko, Seonghyeon
AU - Yang, Huigyu
AU - Bum, Junghyun
AU - Le, Duc Tai
AU - Choo, Hyunseung
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.
PY - 2026
Y1 - 2026
N2 - CardioMetabolic Risk (CMR) assessment requires numerous risk factors derived from anthropometric measurements, sphygmomanometry, and blood tests. Deep Learning enables CMR factors to be acquirable from a medical image (e.g., fundus), however, model-per-factor approach is insufficient solution in cost-efficiency. It is also challenge to predict multiple factors simultaneously from a single image, since the CMR factors are inter-correlated among themselves but also correlated with fundus features in various depths. To address this challenge, we propose Self-Propagative multi-task Learning (SePL) which utilizes comparatively simple 6 CMR factor predictions as prior knowledge to guide predicting more complex CMR factors. The proposed SePL propagates its initial predictions to a latent space, enriching unimodal features into multimodal representation. A discriminative mixture of experts leverages the relevant prior for 9 CMR factor predictions. The training and testing of SePL use 5,232 sets of fundus images and corresponding CMR factors. Experimental results demonstrate that the proposed SePL outperforms the existing methods up to 10.46% of AUC and 8.07% of MAE across all 15 CMR factor predictions. The code is available at https://github.com/shko0215/SePL.
AB - CardioMetabolic Risk (CMR) assessment requires numerous risk factors derived from anthropometric measurements, sphygmomanometry, and blood tests. Deep Learning enables CMR factors to be acquirable from a medical image (e.g., fundus), however, model-per-factor approach is insufficient solution in cost-efficiency. It is also challenge to predict multiple factors simultaneously from a single image, since the CMR factors are inter-correlated among themselves but also correlated with fundus features in various depths. To address this challenge, we propose Self-Propagative multi-task Learning (SePL) which utilizes comparatively simple 6 CMR factor predictions as prior knowledge to guide predicting more complex CMR factors. The proposed SePL propagates its initial predictions to a latent space, enriching unimodal features into multimodal representation. A discriminative mixture of experts leverages the relevant prior for 9 CMR factor predictions. The training and testing of SePL use 5,232 sets of fundus images and corresponding CMR factors. Experimental results demonstrate that the proposed SePL outperforms the existing methods up to 10.46% of AUC and 8.07% of MAE across all 15 CMR factor predictions. The code is available at https://github.com/shko0215/SePL.
KW - Discriminative Mixture of Experts
KW - Multi-task Learning
KW - Multimodal Learning
KW - Self-Propagation
UR - https://www.scopus.com/pages/publications/105017953728
U2 - 10.1007/978-3-032-05182-0_55
DO - 10.1007/978-3-032-05182-0_55
M3 - Conference contribution
AN - SCOPUS:105017953728
SN - 9783032051813
T3 - Lecture Notes in Computer Science
SP - 564
EP - 573
BT - Medical Image Computing and Computer Assisted Intervention, MICCAI 2025 - 28th International Conference, Proceedings
A2 - Gee, James C.
A2 - Hong, Jaesung
A2 - Sudre, Carole H.
A2 - Golland, Polina
A2 - Alexander, Daniel C.
A2 - Iglesias, Juan Eugenio
A2 - Venkataraman, Archana
A2 - Kim, Jong Hyo
PB - Springer Science and Business Media Deutschland GmbH
T2 - 28th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2025
Y2 - 23 September 2025 through 27 September 2025
ER -