TE-SSL: Time and Event-aware Self Supervised Learning for Alzheimer’s Disease Progression Analysis
Published in MICCAI 2024, 2024
We introduce TE-SSL, a time and event-aware self-supervised learning framework for Alzheimer’s disease progression analysis. By incorporating time-to-event and event data as supervisory signals, TE-SSL improves representation learning and outperforms existing SSL methods in downstream survival analysis tasks.
Citation: Thrasher, J., Devkota, A., Tafti, A.P., Bhattarai, B., Gyawali, P. and Alzheimer’s Disease Neuroimaging Initiative, 2024, October. TE-SSL: Time and Event-aware Self Supervised Learning for Alzheimer’s Disease Progression Analysis. In International Conference on Medical Image Computing and Computer-Assisted Intervention (pp. 324-333). Cham: Springer Nature Switzerland.
Citation: Thrasher, J., Devkota, A., Tafti, A.P., Bhattarai, B., Gyawali, P. and Alzheimer’s Disease Neuroimaging Initiative, 2024, October. TE-SSL: Time and Event-aware Self Supervised Learning for Alzheimer’s Disease Progression Analysis. In International Conference on Medical Image Computing and Computer-Assisted Intervention (pp. 324-333). Cham: Springer Nature Switzerland.
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