Before revision:  (ii) the systematic adoption of training over collection of subregions (i.e. "tiles" or "patches") extracted for the same subject. We verify thataccuracy scores may be inflated up to 41\% , even if a well-designed 10x5 iterated cross-validation DAP is applied, unless all images from the same subject are kept together either in the internal training or validation splits.
After revision:  (ii) the systematic adoption of training over collection of subregions (i.e. "tiles" ) are considered. Despite this aspect is largely recognized by the community, we argue that it is often overlooked. In this study, we assess the impact of data leakage on the performance of machine learning models trained and validated on multiple histology data collection. We prove that, even with a properly designed DAP (10x5 repeated cross-validation), predictive scores can be inflated up to 41\% , even if a well-designed 10x5 iterated cross-validation DAP is applied, unless all images from the same subject are kept together either in the internal training or validation splits.