title: On the convergence of nanotechnology and Big Data analysis for computer-aided diagnosis.
abstract: An overview is provided of the challenges involved in building computer-aided diagnosis systems capable of precise medical diagnostics based on integration and interpretation of data from different sources and formats. The availability of massive amounts of data and computational methods associated with the Big Data paradigm has brought hope that such systems may soon be available in routine clinical practices, which is not the case today. We focus on visual and machine learning analysis of medical data acquired with varied nanotech-based techniques and on methods for Big Data infrastructure. Because diagnosis is essentially a classification task, we address the machine learning techniques with supervised and unsupervised classification, making a critical assessment of the progress already made in the medical field and the prospects for the near future. We also advocate that successful computer-aided diagnosis requires a merge of methods and concepts from nanotechnology and Big Data analysis.
authors.affiliation: Institute of Mathematics & Computer Science, University of Sao Paulo (USP), 13560-970 Sao Carlos, SP, Brazil.
authors.forename: Jose F
authors.initials: JF
authors.lastname: Rodrigues
authors.affiliation: Institute of Mathematics & Computer Science, University of Sao Paulo (USP), 13560-970 Sao Carlos, SP, Brazil.
authors.forename: Fernando V
authors.initials: FV
authors.lastname: Paulovich
authors.affiliation: Institute of Mathematics & Computer Science, University of Sao Paulo (USP), 13560-970 Sao Carlos, SP, Brazil.
authors.forename: Maria Cf
authors.initials: MC
authors.lastname: de Oliveira
authors.affiliation: Sao Carlos Institute of Physics, University of Sao Paulo (USP), CP 369, 13560-970 Sao Carlos, SP, Brazil.
authors.forename: Osvaldo N
authors.initials: ON
authors.lastname: de Oliveira
mesh_terms: D000067917:Cloud Computing; D057225:Data Mining / Q000379:methods; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D006801:Humans; D000069550:Machine Learning; D008499:Medical Records; D036103:Nanotechnology / Q000379:methods
keywords: Big Data; biosensors; computer-aided diagnosis; data analysis; data visualization; healthcare; nanotechnology