Predicting Visual Improvement after Macular Hole Surgery: a Cautionary Tale on Deep Learning with Very Limited Data
This work highlights the challenges of applying deep learning in medical imaging with very limited data, which is an incremental cautionary insight for researchers in this domain.
The study tackled the problem of predicting visual improvement after macular hole surgery using preoperative data, finding that deep learning models underperformed compared to a simple regression model on clinical features, with only 121 samples available.
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.