Harnessing Intra-group Variations Via a Population-Level Context for Pathology Detection
<|reference_start|>Harnessing Intra-group Variations Via a Population-Level Context for Pathology Detection: 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.<|reference_end|>
@article{githinji2024harnessing,
    title={Harnessing Intra-group Variations Via a Population-Level Context for
  Pathology Detection},
    author={P. Bilha Githinji, Xi Yuan, Zhenglin Chen, Ijaz Gul, Dingqi Shang, Wen
  Liang, Jianming Deng, Dan Zeng, Dongmei yu, Chenggang Yan, Peiwu Qin},
    journal={arXiv preprint arXiv:2403.02307},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.02307},
    primaryClass={eess.IV cs.CV}
}