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. Proof-of-concept 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.
@article{arxiv.2403.02307,
title = {Harnessing Intra-group Variations Via a Population-Level Context for Pathology Detection},
author = {P. Bilha Githinji and Xi Yuan and Zhenglin Chen and Ijaz Gul and Dingqi Shang and Wen Liang and Jianming Deng and Dan Zeng and Dongmei yu and Chenggang Yan and Peiwu Qin},
journal= {arXiv preprint arXiv:2403.02307},
year = {2024}
}