DeepMiner: Discovering Interpretable Representations for Mammogram Classification and Explanation

31 May 2018Jimmy WuBolei ZhouDiondra PeckScott HsiehVandana DialaniLester MackeyGenevieve Patterson

We propose DeepMiner, a framework to discover interpretable representations in deep neural networks and to build explanations for medical predictions. By probing convolutional neural networks (CNNs) trained to classify cancer in mammograms, we show that many individual units in the final convolutional layer of a CNN respond strongly to diseased tissue concepts specified by the BI-RADS lexicon... (read more)

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