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

• URL: http://arxiv.org/abs/2403.02307v2
• Date: Thu, 25 Jul 2024 04:40:04 GMT
• Title: Harnessing Intra-group Variations Via a Population-Level Context for Pathology Detection
• Authors: P. Bilha Githinji, Xi Yuan, Zhenglin Chen, Ijaz Gul, Dingqi Shang, Wen Liang, Jianming Deng, Dan Zeng, Dongmei yu, Chenggang Yan, Peiwu Qin,
• Abstract summary: 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. 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.
• Score: 17.87825422578005
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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