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Multiple instance learning (MIL) models have achieved remarkable success inanalyzing whole slide images (WSIs) for disease classification problems.However, with regard to gigapixel WSI classification problems, current MILmodels are often incapable of differentiating a WSI with extremely small tumorlesions. This minute tumor-to-normal area ratio in a MIL bag inhibits theattention mechanism from properly weighting the areas corresponding to minortumor lesions. To overcome this challenge, we propose salient instanceinference MIL (SiiMIL), a weakly-supervised MIL model for WSI classification.Our method initially learns representations of normal WSIs, and it thencompares the normal WSIs representations with all the input patches to inferthe salient instances of the input WSI. Finally, it employs attention-based MILto perform the slide-level classification based on the selected patches of theWSI. 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 tonormal instances within a bag can increase by two to four times. It is worthmentioning that it performs equally well for large tumor lesions. As a result,SiiMIL achieves a significant improvement in performance over thestate-of-the-art MIL methods.

Attention2Minority: A salient instance inference-based multiple instance learning for classifying small lesions in whole slide images