Abstract
We investigate the relational classification of biological cells in 2D microscopy images. Rather than treating each cell image independently, we investigate whether and how the neighborhood information of a cell can be informative for its prediction. We propose a Relational Long Short-Term Memory (R-LSTM) algorithm, coupled with auto-encoders and convolutional neural networks, that can learn from both annotated and unlabeled microscopy images and that can utilize both the local and neighborhood information to perform an improved classification of biological cells. Experimental results on both synthetic and real datasets show that R-LSTM performs comparable to or better than six baselines.
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