  Multiple instance learning (MIL) has been increasingly used in the
classification of histopathology whole slide images (WSIs). However, MIL
approaches for this specific classification problem still face unique
challenges, particularly those related to small sample cohorts. In these, there
are limited number of WSI slides (bags), while the resolution of a single WSI
is huge, which leads to a large number of patches (instances) cropped from this
slide. To address this issue, we propose to virtually enlarge the number of
bags by introducing the concept of pseudo-bags, on which a double-tier MIL
framework is built to effectively use the intrinsic features. Besides, we also
contribute to deriving the instance probability under the framework of
attention-based MIL, and utilize the derivation to help construct and analyze
the proposed framework. The proposed method outperforms other latest methods on
the CAMELYON-16 by substantially large margins, and is also better in
performance on the TCGA lung cancer dataset. The proposed framework is ready to
be extended for wider MIL applications. The code is available at:
https://github.com/hrzhang1123/DTFD-MIL
