Harnessing Intra-group Variations Via a Population-Level Context for
  Pathology Detection
P. Bilha Githinji Xi Yuan Zhenglin Chen Ijaz Gul Dingqi Shang Wen Liang Jianming Deng Dan Zeng Dongmei yu Chenggang Yan Peiwu Qin
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.