Automated Detection of Myopic Maculopathy in MMAC 2023: Achievements in
  Classification, Segmentation, and Spherical Equivalent Prediction
Yihao Li Philippe Zhang Yubo Tan Jing Zhang Zhihan Wang Weili Jiang Pierre-Henri Conze Mathieu Lamard Gwenolé Quellec Mostafa El Habib Daho
Myopic macular degeneration is the most common complication of myopia and the
primary cause of vision loss in individuals with pathological myopia. Early
detection and prompt treatment are crucial in preventing vision impairment due
to myopic maculopathy. This was the focus of the Myopic Maculopathy Analysis
Challenge (MMAC), in which we participated. In task 1, classification of myopic
maculopathy, we employed the contrastive learning framework, specifically
SimCLR, to enhance classification accuracy by effectively capturing enriched
features from unlabeled data. This approach not only improved the intrinsic
understanding of the data but also elevated the performance of our
classification model. For Task 2 (segmentation of myopic maculopathy plus
lesions), we have developed independent segmentation models tailored for
different lesion segmentation tasks and implemented a test-time augmentation
strategy to further enhance the model's performance. As for Task 3 (prediction
of spherical equivalent), we have designed a deep regression model based on the
data distribution of the dataset and employed an integration strategy to
enhance the model's prediction accuracy. The results we obtained are promising
and have allowed us to position ourselves in the Top 6 of the classification
task, the Top 2 of the segmentation task, and the Top 1 of the prediction task.
The code is available at
\url{https://github.com/liyihao76/MMAC_LaTIM_Solution}.