title: Large-Scale medical image analytics: Recent methodologies, applications and Future directions.
abstract: Despite the ever-increasing amount and complexity of annotated medical image data, the development of large-scale medical image analysis algorithms has not kept pace with the need for methods that bridge the semantic gap between images and diagnoses. The goal of this position paper is to discuss and explore innovative and large-scale data science techniques in medical image analytics, which will benefit clinical decision-making and facilitate efficient medical data management. Particularly, we advocate that the scale of image retrieval systems should be significantly increased at which interactive systems can be effective for knowledge discovery in potentially large databases of medical images. For clinical relevance, such systems should return results in real-time, incorporate expert feedback, and be able to cope with the size, quality, and variety of the medical images and their associated metadata for a particular domain. The design, development, and testing of the such framework can significantly impact interactive mining in medical image databases that are growing rapidly in size and complexity and enable novel methods of analysis at much larger scales in an efficient, integrated fashion.
authors: Department of Computer Science, University of North Carolina at Charlotte, Charlotte, NC, 28223, USA.
authors: Shaoting
authors: S
authors: Zhang
authors: Department of Computer Science, Rutgers University, Piscataway, NJ, 08854, USA. Electronic address: dnm@cs.rutgers.edu.
authors: Dimitris
authors: D
authors: Metaxas
mesh_terms: D000465:Algorithms; D016208:Databases, Factual; D003936:Diagnosis, Computer-Assisted / Q000379:methods* / Q000639:trends*; D003952:Diagnostic Imaging / Q000379:methods* / Q000639:trends*; D006801:Humans; D016247:Information Storage and Retrieval
keywords: Image retrieval; Large-scale; Medical image analytics; Segmentation; Visual analytics