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Deep Learning for Medical Image Processing: Overview, Challenges and the Future

Dey, Nilanjan ; Ashour, Amira S ; Borra, Surekha

Classification in BioApps, 2018, Vol.26, p.323-350

Switzerland: Springer International Publishing AG

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  • Título:
    Deep Learning for Medical Image Processing: Overview, Challenges and the Future
  • Autor: Dey, Nilanjan ; Ashour, Amira S ; Borra, Surekha
  • Assuntos: Biomedical engineering ; Computer vision ; Deep learning ; Image analysis ; Medical image analysis ; Pharmaceutical technology
  • É parte de: Classification in BioApps, 2018, Vol.26, p.323-350
  • Descrição: The health care sector is totally different from any other industry. It is a high priority sector and consumers expect the highest level of care and services regardless of cost. The health care sector has not achieved society’s expectations, even though the sector consumes a huge percentage of national budgets. Mostly, the interpretations of medical data are analyzed by medical experts. In terms of a medical expert interpreting images, this is quite limited due to its subjectivity and the complexity of the images; extensive variations exist between experts and fatigue sets in due to their heavy workload. Following the success of deep learning in other real-world applications, it is seen as also providing exciting and accurate solutions for medical imaging, and is seen as a key method for future applications in the health care sector. In this chapter, we discuss state-of-the-art deep learning architecture and its optimization when used for medical image segmentation and classification. The chapter closes with a discussion of the challenges of deep learning methods with regard to medical imaging and open research issue.
  • Títulos relacionados: Lecture Notes in Computational Vision and Biomechanics
  • Editor: Switzerland: Springer International Publishing AG
  • Idioma: Inglês
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  • Realização: ABCD-USP USP   Logos de Redes Sociais: Freepik

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