Discriminative Subtyping of Lung Cancers from Histopathology Images via Contextual Deep Learning. Lengerich*, B., Al-Shedivat*, M., Alavi, A., Williams, J., Labakki, S., & Xing, E. 2020.
Discriminative Subtyping of Lung Cancers from Histopathology Images via Contextual Deep Learning [link]Preprint  abstract   bibtex   
Summarizing multiple data modalities into a parsimonious cancer “subtype” is difficult because the most informative representation of each patient’s disease is not observed. We propose to model these latent summaries as discriminative subtypes: sample representations which induce accurate and interpretable sample-specific models for downstream predictions. In this way, discriminative subtypes, which are shared between data modalities, can be estimated from one data modality and optimized according to the predictions induced in another modality. We apply this approach to lung cancer by training a deep neural network to predict discriminative subtypes from histopathology images, and use these predicted subtypes to generate models which classify adenocarcinoma, squamous cell carcinoma, and healthy tissue based on transcriptomic signatures. In this way, we optimize the latent discriminative subtypes through induced prediction loss, and the discriminative subtypes are interpreted with standard interpretation of transcriptomic predictive models. Our framework achieves state-of-the-art classification accuracy (F1-score of 0.97) and identifies discriminative subtypes which link histopathology images to transcriptomic explanations without requiring pre-specification of morphological patterns or transcriptomic processes.
@article{lengerich2020discriminative,
  title={Discriminative Subtyping of Lung Cancers from Histopathology Images via Contextual Deep Learning},
  author={Lengerich*, Benjamin and Al-Shedivat*, Maruan and Alavi, Amir and Williams, Jennifer and Labakki, Sami and Xing, Eric},
  year={2020},
  informal_venue = {arXiv},
  url_preprint={https://www.medrxiv.org/content/10.1101/2020.06.25.20140053v2},
  abstract={Summarizing multiple data modalities into a parsimonious cancer “subtype” is difficult because the most informative representation of each patient’s disease is not observed. We propose to model these latent summaries as discriminative subtypes: sample representations which induce accurate and interpretable sample-specific models for downstream predictions. In this way, discriminative subtypes, which are shared between data modalities, can be estimated from one data modality and optimized according to the predictions induced in another modality. We apply this approach to lung cancer by training a deep neural network to predict discriminative subtypes from histopathology images, and use these predicted subtypes to generate models which classify adenocarcinoma, squamous cell carcinoma, and healthy tissue based on transcriptomic signatures. In this way, we optimize the latent discriminative subtypes through induced prediction loss, and the discriminative subtypes are interpreted with standard interpretation of transcriptomic predictive models. Our framework achieves state-of-the-art classification accuracy (F1-score of 0.97) and identifies discriminative subtypes which link histopathology images to transcriptomic explanations without requiring pre-specification of morphological patterns or transcriptomic processes.},
  keywords={Interpretable, Contextualized, Computational Genomics, Cancer},
}
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