ArXiv Preprint
The survival analysis on histological whole-slide images (WSIs) is one of the
most important means to estimate patient prognosis. Although many
weakly-supervised deep learning models have been developed for gigapixel WSIs,
their potential is generally restricted by classical survival analysis rules
and fully-supervision requirements. As a result, these models provide patients
only with a completely-certain point estimation of time-to-event, and they
could only learn from the well-annotated WSI data currently at a small scale.
To tackle these problems, we propose a novel adversarial multiple instance
learning (AdvMIL) framework. This framework is based on adversarial
time-to-event modeling, and it integrates the multiple instance learning (MIL)
that is much necessary for WSI representation learning. It is a plug-and-play
one, so that most existing WSI-based models with embedding-level MIL networks
can be easily upgraded by applying this framework, gaining the improved ability
of survival distribution estimation and semi-supervised learning. Our extensive
experiments show that AdvMIL could not only bring performance improvement to
mainstream WSI models at a relatively low computational cost, but also enable
these models to learn from unlabeled data with semi-supervised learning. Our
AdvMIL framework could promote the research of time-to-event modeling in
computational pathology with its novel paradigm of adversarial MIL.
Pei Liu, Luping Ji, Feng Ye, Bo Fu
2022-12-13
