2021
DOI: 10.48550/arxiv.2101.07429
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Learning Efficient, Explainable and Discriminative Representations for Pulmonary Nodules Classification

Abstract: Automatic pulmonary nodules classification is significant for early diagnosis of lung cancers. Recently, deep learning techniques have enabled remarkable progress in this field. However, these deep models are typically of high computational complexity and work in a black-box manner. To combat these challenges, in this work, we aim to build an efficient and (partially) explainable classification model. Specially, we use neural architecture search (NAS) to automatically search 3D network architectures with excel… Show more

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