Challenges of Medical Text and Image Processing

Meghna Das, Pragya Parmita Sambodhi, Ayushi Khare, S. Naik
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Abstract

The processing of text and images has experienced expansion, and has been such a research area which attracts expertise from various domains, for example, medical, mathematics and computer. The advancement of Machine Learning and Deep Learning models has positively affected the medical domain. Computer processing with the help of diagnostics has already become a foremost part of clinical routine. As the use of technology advances, greater challenges appear. Some examples are the processing and analysis of a significant volume of images, the inadequacy of annotated data and the unavailability of algorithms on public domains for the use of information in the disease detection process. Other challenges include the suspicion of the community on the automatic technology and lack of awareness in the community. All these challenges need to be overcome for the successful implementation of medical applications. This paper discusses the challenges of medical text and image processing and emphasizes the need to conquer these challenges for the betterment of the community. The principal objective of this research is to discuss the challenges in medical text and image processing amongst the public and to promote interest in further research in this field.
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医学文本和图像处理的挑战
文本和图像的处理经历了扩展,并且已经成为这样一个研究领域,吸引了来自各个领域的专业知识,例如医学,数学和计算机。机器学习和深度学习模型的进步对医疗领域产生了积极的影响。在诊断的帮助下进行计算机处理已经成为临床常规的重要组成部分。随着技术应用的进步,更大的挑战出现了。一些例子是处理和分析大量图像、加注释的数据不足以及在疾病检测过程中使用信息的公共领域没有算法。其他挑战包括社区对自动化技术的怀疑和社区意识的缺乏。要成功实施医疗应用,必须克服所有这些挑战。本文讨论了医学文本和图像处理的挑战,并强调需要克服这些挑战,以改善社区。本研究的主要目的是讨论公众在医学文本和图像处理方面的挑战,并促进对该领域进一步研究的兴趣。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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