Generative Adversarial Networks for Robust Breast Cancer Prognosis Prediction with Limited Data Size.

Accurate cancer patient prognosis stratification is essential for oncologists to recommend proper treatment plans. Deep learning models are capable of providing good prediction power for such stratification. The main challenge is that only a limited number of labeled patients are available for cancer prognosis. To overcome this, we proposed Wasserstein Generative Adversarial Network-based Deep Adversarial Data Augmentation (wDADA) that leverages generative adversarial networks to perform data augmentation and assist in model training. We used the proposed framework to train our model for predicting disease-specific survival (DSS) of breast cancer patients from the METABRIC dataset. We found that wDADA achieved 0.6726± 0.0278, 0.7538±0.0328, and 0.6507 ±0.0248 in terms of accuracy, AUC, and concordance index in predicting 5-year DSS, respectively, which is comparable to our previously proposed Bimodal model (accuracy: 0.6889±0.0159; AUC: 0.7546± 0.0183; concordance index: 0.6542±0.0120), which needs careful calibration and extensive search on pre-trained network architectures. The flexibility of the proposed wDADA allows us to incorporate it with ensemble learning and semi-supervised learning to further improve performance. Our results indicate that it is possible to utilize generative adversarial networks to train deep models in medical applications, wherein only limited data are available.