Este paper trás uma implementação de Deep Learning que se confirmada pode ser um grande avanço na indústria de diagnósticos para os serviços de saúde, dado que através de aprendizado algorítmico podem ser identificados diversos tipos de genes cancerígenos e isso pode conter duas externalidades positivas que são 1) o barateamento e a rapidez no diagnóstico, e 2) reformulação total da estratégia de combate e prevenção de doenças.
Abstract: 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.
Conclusions: We presented a deep generative learning model DeepCancer for detection and classification of inflammatory breast cancer and prostate cancer samples. The features are learned through an adversarial feature learning process and then sent as input to a conventional classifier specific to the objective of interest. After modifications through specified hyperparameters, the model performs quite comparatively well on the task tested on two different datasets. The proposed model utilized cDNA microarray gene expressions to gauge its efficacy. Based on deep generative learning, the tuned discriminator and generator models, D and G respectively, learned to differentiate between the gene signatures without any intermediate manual feature handpicking, indicating that much bigger datasets can be experimented on the proposed model more seamlessly. The DeepCloud model will be a vital aid to the medical imaging community and, ultimately, reduce inflammatory breast cancer and prostate cancer mortality.