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Disentangled representations of microscopy images

Created by
  • Haebom

Author

Jacopo Dapueto, Vito Paolo Pastore, Nicoletta Noceti, Francesca Odone

Outline

In this paper, we propose a decoupled representation learning (DRL) methodology to address the problem that deep neural networks have excellent performance but poor interpretability in microscopic image analysis. Using a DRL framework that transfers learned representations from synthetic data, we demonstrate that it improves the trade-off between accuracy and interpretability on benchmark datasets of three microscopic image domains: plankton, yeast vacuoles, and human cells.

Takeaways, Limitations

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Takeaways:
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We demonstrate that DRL can be used to improve model interpretability in microscopy image classification.
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We demonstrate that transfer learning using synthetic data can improve both performance and interpretability on real data.
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We validate the utility of the DRL methodology in a variety of microscopic image domains (plankton, yeast vacuoles, and human cells).
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Limitations:
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Further research is needed to investigate the generalization performance of the proposed DRL methodology.
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The characteristics of the synthetic data used may affect performance depending on their differences from real data.
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Experiments with more diverse and complex microscopy image data are needed.
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