The effects of sparsity induction methods on attention-based multiple instance learning applied to Camelyon16

Spectral decoupling, weight normalization, and L1 loss have been applied to varying degrees in studies concerning computational pathology. These methods induce sparsity and tend to improve overall model performance. However, no work has combined these methods to improve model performance. The work presented here combines and compares these three methods in an attention-based multiple instance learning model to classify whole slide histopathology images from Camelyon16. We observed that spectral decoupling improves accuracy and area under the curve (AUC), but that weight normalization and L1 loss do not. However, when either of the latter is combined with spectral decoupling, accuracy, and AUC further improve over just spectral decoupling. Finally, we demonstrate that varying the magnitude with which these three methods affect model training considerably affects the resulting testing accuracy and AUC.