  Multiple instance learning (MIL) models have achieved remarkable success in
analyzing whole slide images (WSIs) for disease classification problems.
However, with regard to gigapixel WSI classification problems, current MIL
models are often incapable of differentiating a WSI with extremely small tumor
lesions. This minute tumor-to-normal area ratio in a MIL bag inhibits the
attention mechanism from properly weighting the areas corresponding to minor
tumor lesions. To overcome this challenge, we propose salient instance
inference MIL (SiiMIL), a weakly-supervised MIL model for WSI classification.
Our method initially learns representations of normal WSIs, and it then
compares the normal WSIs representations with all the input patches to infer
the salient instances of the input WSI. Finally, it employs attention-based MIL
to perform the slide-level classification based on the selected patches of the
WSI. Our experiments imply that SiiMIL can accurately identify tumor instances,
which could only take up less than 1% of a WSI, so that the ratio of tumor to
normal instances within a bag can increase by two to four times. It is worth
mentioning that it performs equally well for large tumor lesions. As a result,
SiiMIL achieves a significant improvement in performance over the
state-of-the-art MIL methods.
