The similarity among samples and the discrepancy between clusters are two
crucial aspects of image clustering. However, current deep clustering methods
suffer from the inaccurate estimation of either feature similarity or semantic
discrepancy. In this paper, we present a Semantic Pseudo-labeling-based Image
ClustEring (SPICE) framework, which divides the clustering network into a
feature model for measuring the instance-level similarity and a clustering head
for identifying the cluster-level discrepancy. We design two semantics-aware
pseudo-labeling algorithms, prototype pseudo-labeling, and reliable
pseudo-labeling, which enable accurate and reliable self-supervision over
clustering. Without using any ground-truth label, we optimize the clustering
network in three stages: 1) train the feature model through contrastive
learning to measure the instance similarity, 2) train the clustering head with
the prototype pseudo-labeling algorithm to identify cluster semantics, and 3)
jointly train the feature model and clustering head with the reliable
pseudo-labeling algorithm to improve the clustering performance. Extensive
experimental results demonstrate that SPICE achieves significant improvements
(~10%) over existing methods and establishes the new state-of-the-art
clustering results on six image benchmark datasets in terms of three popular
metrics. Importantly, SPICE significantly reduces the gap between unsupervised
and fully-supervised classification; e.g., there is only a 2% (91.8% vs 93.8%)
accuracy difference on CIFAR-10. Our code has been made publically available at
this https URL

本文提出了一种基于语义伪标签的图像聚类框架，使用两种语义感知的伪标签算法进行自监督学习来测量特征相似性和聚类差异，并成功将无监督分类与有监督分类之间的差距缩小，实现了图像聚类的新突破。

SPICE: 图像聚类的语义伪标记

SPICE: Semantic Pseudo-labeling for Image Clustering

With the long-term rapid increase in incidences of colorectal cancer (CRC),
there is an urgent clinical need to improve risk stratification. The
conventional pathology report is usually limited to only a few
histopathological features. However, most of the tumor microenvironments used
to describe patterns of aggressive tumor behavior are ignored. In this work, we
aim to learn histopathological patterns within cancerous tissue regions that
can be used to improve prognostic stratification for colorectal cancer. To do
so, we propose a self-supervised learning method that jointly learns a
representation of tissue regions as well as a metric of the clustering to
obtain their underlying patterns. These histopathological patterns are then
used to represent the interaction between complex tissues and predict clinical
outcomes directly. We furthermore show that the proposed approach can benefit
from linear predictors to avoid overfitting in patient outcomes predictions. To
this end, we introduce a new well-characterized clinicopathological dataset,
including a retrospective collective of 374 patients, with their survival time
and treatment information. Histomorphological clusters obtained by our method
are evaluated by training survival models. The experimental results demonstrate
statistically significant patient stratification, and our approach outperformed
the state-of-the-art deep clustering methods.

本研究旨在通过自我监督学习方法，学习肿瘤组织内的组织病理模式，以优化结直肠癌的预后分层，并通过新的数据集、患者预后模型和实验证明了其效果优于现有深度聚类方法。