Predicting the progression of disease using machine learning and deep learning - MICCAI 2019 papers

In MICCAI 2019 in Shenzhen, there is a lot of interesting papers about predicting the progression of disease. Because they are related to my current work, I am going to (short)list these kind of papers in this blog post. Furthermore, I will try to categorising these papers based on their approaches of predicting the progression of disease. Feel free to contact me if I missed similar MICCAI 2019 papers about prediction of disease’s progression!

Note: This post will be be updated as soon as I find more relevant papers in MICCAI 2019.

Predicting the progression of disease-specific lesions/regions

The following papers proposed predictive models to predict the progression (and regression) of disease-specific lesions/regions.

• Rachmadi M.F., del C. Valdés-Hernández M., Makin S., Wardlaw J.M., Komura T. “Predicting the Evolution of White Matter Hyperintensities in Brain MRI Using Generative Adversarial Networks and Irregularity Map.” [DOI]
• Bigolin Lanfredi R., Schroeder J.D., Vachet C., Tasdizen T. “Adversarial Regression Training for Visualizing the Progression of Chronic Obstructive Pulmonary Disease with Chest X-Rays.” [DOI]
• Ezzine B.E., Rekik I. “Learning-Guided Infinite Network Atlas Selection for Predicting Longitudinal Brain Network Evolution from a Single Observation.” [DOI]
• Ezhov I. et al. “Neural Parameters Estimation for Brain Tumor Growth Modeling.” [DOI]
• Petersen J. et al. “Deep Probabilistic Modeling of Glioma Growth.” [DOI]

Detecting regions associated with disease progression (i.e., unspecified regions)

• Lei H., Zhao Y., Lei B. “Predicting Early Stages of Neurodegenerative Diseases via Multi-task Low-Rank Feature Learning. [DOI]
• Zhou T. et al. “Inter-modality Dependence Induced Data Recovery for MCI Conversion Prediction.” [DOI]
• Basu S., Wagstyl K., Zandifar A., Collins L., Romero A., Precup D. “Early Prediction of Alzheimer’s Disease Progression Using Variational Autoencoders.” [DOI]
• Lu L. et al. “Improved Prediction of Cognitive Outcomes via Globally Aligned Imaging Biomarker Enrichments over Progressions.” [DOI]
• Ravi D., Alexander D.C., Oxtoby N.P., “Alzheimer’s Disease Neuroimaging Initiative. Degenerative Adversarial NeuroImage Nets: Generating Images that Mimic Disease Progression.” [DOI]
• Lian C., Liu M., Wang L., Shen D. “End-to-End Dementia Status Prediction from Brain MRI Using Multi-task Weakly-Supervised Attention Network.” [DOI]
• Li Q. et al. “Novel Iterative Attention Focusing Strategy for Joint Pathology Localization and Prediction of MCI Progression.” [DOI]

Clinical progression of disease and prognosis

• Jung W., Mulyadi A.W., Suk HI. “Unified Modeling of Imputation, Forecasting, and Prediction for AD Progression.” [DOI]
• Shao W. et al. “Diagnosis-Guided Multi-modal Feature Selection for Prognosis Prediction of Lung Squamous Cell Carcinoma.” [DOI]
• Xing X. et al. “Dynamic Spectral Graph Convolution Networks with Assistant Task Training for Early MCI Diagnosis.” [DOI]
• Yu Y., Parsi B., Speier W., Arnold C., Lou M., Scalzo F. “LSTM Network for Prediction of Hemorrhagic Transformation in Acute Stroke.” [DOI]
• Zhang J., Wang Y. “Continually Modeling Alzheimer’s Disease Progression via Deep Multi-order Preserving Weight Consolidation.” [DOI]

Most papers in PRIME-MICCAI 2019

• PRIME: International Workshop on PRedictive Intelligence In MEdicine [LINK]