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Deep learning has enabled breakthroughs in automated diagnosis from medical imaging, with many successful applications in ophthalmology. However, standard medical image classi cation approaches only assess disease presence at the time of acquisition, neglecting the common clinical setting of longitudinal imaging. For slow, progressive eye diseases like age-related macular degeneration (AMD) and primary open-angle glaucoma (POAG), patients undergo repeated imaging over time to track disease progression and forecasting the future risk of developing a disease is critical to properly plan treatment. Our proposed Longitudinal Transformer for Survival Analysis (LTSA) enables dynamic disease prognosis from longitudinal medical imaging, modeling the time to disease from sequences of fundus photography images captured over long, irregular time periods. Using longitudinal imaging data from the Age-Related Eye Disease Study (AREDS) and Ocular Hypertension Treatment Study (OHTS), LTSA signi cantly outperformed a single-image baseline in 19/20 head-to- head comparisons on late AMD prognosis and 18/20 comparisons on POAG prognosis. A temporal attention analysis also suggested that, while the most recent image is typically the most in uential, prior imaging still provides additional prognostic value.
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Enriched Representation Learning for Longitudinal Chest X-ray Analysis: A Novel Approach for Improved Disease Detection and Localization
Chest X-rays are commonly used for diagnosing and characterizing lung diseases, but the complex morphological patterns in radiographic appearances can challenge clinicians in making accurate diagnoses. To address this challenge, various learning methods have been developed for algorithm-aided disease detection and automated diagnosis. However, most existing methods fail to account for the heterogeneous variability in longitudinal imaging records and the presence of missing or inconsistent temporal data. In this paper, we propose a novel longitudinal learning framework that enriches inconsistent imaging data over sequential time points by leveraging 2D Principal Component Analysis (2D-PCA) and a robust adaptive loss function. We also derive an efficient solution algorithm that ensures both objective and sequence convergence for the non-convex optimization problem. Our experiments on the CheXpert dataset demonstrate improved performance in capturing indicative abnormalities in medical images and achieving satisfactory diagnoses. We believe that our method will be of significant interest to the research community working on medical image analysis.
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- PAR ID:
- 10508018
- Publisher / Repository:
- IEEE
- Date Published:
- Journal Name:
- IEEE ICDM 2023
- ISBN:
- 979-8-3503-0788-7
- Page Range / eLocation ID:
- 1127 to 1132
- Subject(s) / Keyword(s):
- Longitudinal Learning, Representation Enrichment, Robust Learning
- Format(s):
- Medium: X
- Location:
- Shanghai, China
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/ICDM58522.2023.00135
