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Patch Progression Masked Autoencoder with Fusion CNN Network for Classifying Evolution Between Two Pairs of 2D OCT Slices
Created by
Haebom
Author
Philippe Zhang, Weili Jiang, Yihao Li, Jing Zhang, Sarah Matta, Yubo Tan, Hui Lin, Haoshen Wang, Jiangtian Pan, Hui Xu, Laurent Borderie, Alexandre Le Guilcher, B eatrice Cochener, Chubin Ou, Gwenol e Quellec, Mathieu Lamard
Outline
This paper reports the results of our participation in the MARIO Challenge, a medical image analysis competition for monitoring the progression of age-related macular degeneration (AMD). Specifically, we focused on tracking the progression of neovascularization in OCT scans of patients with wet AMD to develop personalized treatment plans. In Task 1, we applied a fusion CNN network using an ensemble of models to classify progression between two pairs of 2D slices in sequential OCT scans. In Task 2, we proposed a Patch Progression Masked Autoencoder to generate an OCT for the next examination and classify progression between the OCT generated using the solution from Task 1 and the current OCT to predict progression over the next three months based on current examination data. While we placed within the top 10 in both tasks, we were ineligible for awards because some of our team members were affiliated with the challenge organizers.
Takeaways, Limitations
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Takeaways:
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Demonstrating the effectiveness of a model for predicting the progression of AMD using fusion CNN and model ensemble techniques.
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A future progression prediction model presented using Patch Progression Masked Autoencoder.
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Verify the competitiveness of the proposed model by achieving top scores in the MARIO Challenge.
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Limitations:
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Disqualification of some team members from receiving awards due to their affiliation with the organizer.
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Further research is needed to evaluate the generalization performance and clinical utility of the proposed model.
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Lack of detailed model structure and hyperparameter information.