Title: When high-performing models behave poorly in practice: periodic sampling can help. Abstract: Training a deep neural network (DNN) for breast cancer detection from medical images suffers from the (hopefully) low prevalence of the pathology.
For a sensible amount of positive cases, images must be collected from numerous places resulting in large heterogeneous datasets with different acquisition devices, populations, cancer incidences.
Without precaution, this heterogeneity may result in a DNN biased by latent variables a priori independent of the pathology.
This may be dramatic if this DNN is used inside a software to help radiologists to detect cancers.
This work mitigates this issue by acting on how mini-batches for Stochastic Gradient Descent (SGD) algorithms are constructed.
The dataset is divided into homogeneous subsets sharing some attributes (\textit{e.g.} acquisition device, source) called Data Segments (DSs).
Batches are built by sampling each DS periodically with a frequency proportional to the rarest label in the DS and by simultaneously preserving an overall balance between positive and negative labels within the batch.
Periodic sampling is compared to balanced sampling (equal amount of labels within a batch, independently of DS) and to balanced sampling within DS (equal amount of labels within a batch and each DS).
We show, on breast cancer prediction from mammography images of various devices and origins, that periodic sampling leads to better generalization than other sampling strategies.