Dynamic Entity-Masked Graph Diffusion Model for histopathological image Representation Learning
Journal:
arXiv
Published Date:
Dec 13, 2024
Abstract
Significant disparities between the features of natural images and those
inherent to histopathological images make it challenging to directly apply and
transfer pre-trained models from natural images to histopathology tasks.
Moreover, the frequent lack of annotations in histopathology patch images has
driven researchers to explore self-supervised learning methods like mask
reconstruction for learning representations from large amounts of unlabeled
data. Crucially, previous mask-based efforts in self-supervised learning have
often overlooked the spatial interactions among entities, which are essential
for constructing accurate representations of pathological entities. To address
these challenges, constructing graphs of entities is a promising approach. In
addition, the diffusion reconstruction strategy has recently shown superior
performance through its random intensity noise addition technique to enhance
the robust learned representation. Therefore, we introduce H-MGDM, a novel
self-supervised Histopathology image representation learning method through the
Dynamic Entity-Masked Graph Diffusion Model. Specifically, we propose to use
complementary subgraphs as latent diffusion conditions and self-supervised
targets respectively during pre-training. We note that the graph can embed
entities' topological relationships and enhance representation. Dynamic
conditions and targets can improve pathological fine reconstruction. Our model
has conducted pretraining experiments on three large histopathological
datasets. The advanced predictive performance and interpretability of H-MGDM
are clearly evaluated on comprehensive downstream tasks such as classification
and survival analysis on six datasets. Our code will be publicly available at
https://github.com/centurion-crawler/H-MGDM.
