Realizing sufficient separability between the distributions of healthy and
pathological samples is a critical obstacle for pathology detection
convolutional models. Moreover, these models exhibit a bias for contrast-based
images, with diminished performance on texture-based medical images. This study
introduces the notion of a population-level context for pathology detection and
employs a graph theoretic approach to model and incorporate it into the latent
code of an autoencoder via a refinement module we term PopuSense. PopuSense
seeks to capture additional intra-group variations inherent in biomedical data
that a local or global context of the convolutional model might miss or smooth
out. Experiments on contrast-based and texture-based images, with minimal
adaptation, encounter the existing preference for intensity-based input.
Nevertheless, PopuSense demonstrates improved separability in contrast-based
images, presenting an additional avenue for refining representations learned by
a model.

通过引入人口层面的上下文并运用图论方法，将其整合到自动编码器的潜在编码中，本研究提出了一种称为 PopuSense 的改进模块，以捕捉生物医学数据中卷积模型可能忽略或平滑的额外组内变异。在对比度和纹理为基础的图像上进行的实验表明，PopuSense 在对比度为基础的图像中展现了更好的可分性，为模型所学习的表示提供了额外的改进途径。

利用种群层上下文来利用群组内变异性进行病理检测

Harnessing Intra-group Variations Via a Population-Level Context for  Pathology Detection

A graph theoretic approach is proposed for object shape representation in a
hierarchical compositional architecture called Compositional Hierarchy of Parts
(CHOP). In the proposed approach, vocabulary learning is performed using a
hybrid generative-descriptive model. First, statistical relationships between
parts are learned using a Minimum Conditional Entropy Clustering algorithm.
Then, selection of descriptive parts is defined as a frequent subgraph
discovery problem, and solved using a Minimum Description Length (MDL)
principle. Finally, part compositions are constructed by compressing the
internal data representation with discovered substructures. Shape
representation and computational complexity properties of the proposed approach
and algorithms are examined using six benchmark two-dimensional shape image
datasets. Experiments show that CHOP can employ part shareability and indexing
mechanisms for fast inference of part compositions using learned shape
vocabularies. Additionally, CHOP provides better shape retrieval performance
than the state-of-the-art shape retrieval methods.

提出了一种基于图论的方法，用于在由部分组成的分层组合架构中表示对象形状，通过统计模型和最小描述长度原则，在受限制的形状单词表达中学习形状的描述，并将内部数据表示与发现的子结构压缩来构建部分组成。在六个基准二维形状图像数据集上验证了该方法。实验证明，Compositional Hierarchy of Parts（CHOP）可以使用部分共享和索引机制快速推断出形状组成，并提供比现有技术更好的形状检索性能。