Dense mapping of breast cancer tissue components through crowdsourcing

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In this presentation, Lee Cooper describes a crowdsourcing approach in data collection for nucleus classification, localization and segmentation in breast cancer. He reviews a protocol that enables scalable generation of nuclear boundaries without laborious manual tracing and demonstrates that algorithmic suggestions improve the accuracy of non-expert annotations.  A technique called Decision Tree Approximation of Learned Embeddings leverages nucleus segmentation data to improve the interpretability and the adoption of nucleus classification models is also outlined.


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