DeepCancer: Detecting Cancer through Gene Expressions via Deep Generative Learning
This work addresses cancer diagnosis for medical applications, but it is incremental as it applies existing deep generative methods to a specific domain.
The authors tackled cancer diagnosis using gene expression data by proposing DeepCancer, a deep generative model that learns features from unlabeled microarray data and integrates with conventional classifiers, achieving high precision and significantly controlling false positives and false negatives on two clinical datasets.
Transcriptional profiling on microarrays to obtain gene expressions has been used to facilitate cancer diagnosis. We propose a deep generative machine learning architecture (called DeepCancer) that learn features from unlabeled microarray data. These models have been used in conjunction with conventional classifiers that perform classification of the tissue samples as either being cancerous or non-cancerous. The proposed model has been tested on two different clinical datasets. The evaluation demonstrates that DeepCancer model achieves a very high precision score, while significantly controlling the false positive and false negative scores.