预测黄斑裂孔手术后的视力改善：数据非常有限的深度学习警示故事
Predicting Visual Improvement after Macular Hole Surgery: a Cautionary Tale on Deep Learning with Very Limited Data
摘要：
我们研究了机器学习模型从术前数据（视网膜图像和临床特征）预测黄斑裂孔手术后视力改善的潜力。收集我们自己的数据，我们最终只有121个样本，使我们的工作在非常有限的数据制度。我们探索了各种针对有限数据的深度学习方法来训练深度计算机视觉模型，发现所有测试的深度视觉模型在临床特征上都优于简单的回归模型。我们认为这是一个令人信服的证据，证明了在非常有限的数据上使用深度学习的极端困难。
英文摘要：
We investigate the potential of machine learning models for the prediction of visual improvement after macular hole surgery from preoperative data (retinal images and clinical features). Collecting our own data for the task, we end up with only 121 total samples, putting our work in the very limited data regime. We explore a variety of deep learning methods for limited data to train deep computer vision models, finding that all tested deep vision models are outperformed by a simple regression model on the clinical features. We believe this is compelling evidence of the extreme difficulty of using deep learning on very limited data.
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