AdvMIL: Adversarial Multiple Instance Learning for the Survival Analysis
on Whole-Slide Images
- URL: http://arxiv.org/abs/2212.06515v2
- Date: Wed, 5 Apr 2023 04:36:51 GMT
- Title: AdvMIL: Adversarial Multiple Instance Learning for the Survival Analysis
on Whole-Slide Images
- Authors: Pei Liu, Luping Ji, Feng Ye, and Bo Fu
- Abstract summary: We propose a novel adversarial multiple instance learning (AdvMIL) framework.
This framework is based on adversarial time-to-event modeling, and integrates the multiple instance learning (MIL) that is much necessary for WSI representation learning.
Our experiments show that AdvMIL not only could bring performance improvement to mainstream WSI survival analysis methods at a relatively low computational cost, but also enables these methods to effectively utilize unlabeled data via semi-supervised learning.
- Score: 12.09957276418002
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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-supervised learning 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 labeled 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 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 MIL-based end-to-end methods can be easily upgraded
by applying this framework, gaining the improved abilities of survival
distribution estimation and semi-supervised learning. Our extensive experiments
show that AdvMIL not only could often bring performance improvement to
mainstream WSI survival analysis methods at a relatively low computational
cost, but also enables these methods to effectively utilize unlabeled data via
semi-supervised learning. Moreover, it is observed that AdvMIL could help
improving the robustness of models against patch occlusion and two
representative image noises. The proposed AdvMIL framework could promote the
research of survival analysis in computational pathology with its novel
adversarial MIL paradigm.
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