A deep learning model trained on only eight whole-slide images accurately segments tumors: wise data use versus big data

Abstract

Computer-assisted pathology is one of the biggest challenges in the medicine of the future. However, artificial intelligence is struggling to gain acceptance in the broader medical community due to data security issues, lack of trust in the machine, and poor data availability. Here, we develop a tumor delineation algorithm with only eight whole slide images of ovarian cancer to demonstrate the feasibility of an artificial intelligence application created from only a few data, finely annotated and with optimal processing. We test the model on seventeen other slides from the same hospital. The predictions are similar to the ground truth annotations made by an expert pathologist, with a mean DICE score of 0.90 [0.85 - 0.93]. The results on slides from another hospital are consistent, suggesting that the model is generalizable and that its performance does not suffer from different data acquisition. This study demonstrates the feasibility of a contouring algorithm based on a reduced dataset well optimized, going against the commonly accepted idea that a phenomenal amount of data is paramount. This study paves the way for other medical applications, especially for rare pathologies with limited available data.