SI-MIL: Taming Deep MIL for Self-Interpretability in Gigapixel
  Histopathology
Saarthak Kapse Pushpak Pati Srijan Das Jingwei Zhang Chao Chen Maria Vakalopoulou Joel Saltz Dimitris Samaras Rajarsi R. Gupta Prateek Prasanna
Introducing interpretability and reasoning into Multiple Instance Learning
(MIL) methods for Whole Slide Image (WSI) analysis is challenging, given the
complexity of gigapixel slides. Traditionally, MIL interpretability is limited
to identifying salient regions deemed pertinent for downstream tasks, offering
little insight to the end-user (pathologist) regarding the rationale behind
these selections. To address this, we propose Self-Interpretable MIL (SI-MIL),
a method intrinsically designed for interpretability from the very outset.
SI-MIL employs a deep MIL framework to guide an interpretable branch grounded
on handcrafted pathological features, facilitating linear predictions. Beyond
identifying salient regions, SI-MIL uniquely provides feature-level
interpretations rooted in pathological insights for WSIs. Notably, SI-MIL, with
its linear prediction constraints, challenges the prevalent myth of an
inevitable trade-off between model interpretability and performance,
demonstrating competitive results compared to state-of-the-art methods on
WSI-level prediction tasks across three cancer types. In addition, we
thoroughly benchmark the local- and global-interpretability of SI-MIL in terms
of statistical analysis, a domain expert study, and desiderata of
interpretability, namely, user-friendliness and faithfulness.