Healthcare sector is totally different from other industry. It is on high
priority sector and people expect highest level of care and services regardless
of cost. It did not achieve social expectation even though it consume huge
percentage of budget. Mostly the interpretations of medical data is being done
by medical expert. In terms of image interpretation by human expert, it is
quite limited due to its subjectivity, the complexity of the image, extensive
variations exist across different interpreters, and fatigue. After the success
of deep learning in other real world application, it is also providing exciting
solutions with good accuracy for medical imaging and is seen as a key method
for future applications in health secotr. In this chapter, we discussed state
of the art deep learning architecture and its optimization used for medical
image segmentation and classification. In the last section, we have discussed
the challenges deep learning based methods for medical imaging and open
research issue.

这篇研究论文介绍了深度学习在医学图像分割和分类中的最新进展和应用，并探讨了基于深度学习的方法在医学成像中面临的挑战和未解决研究问题。