Large Foundation Model for Cancer Segmentation.

IF 2.7 4区 医学 Q3 ONCOLOGY Technology in Cancer Research & Treatment Pub Date : 2024-01-01 DOI:10.1177/15330338241266205
Zeyu Ren, Yudong Zhang, Shuihua Wang
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引用次数: 0

Abstract

Recently, large language models such as ChatGPT have made huge strides in understanding and generating human-like text and have demonstrated considerable success in natural language processing. These foundation models also perform well in computer vision. However, there is a growing need to use these technologies for specific medical tasks, especially for identifying cancer in images. This paper looks at how these foundation models, such as the segment anything model, could be used for cancer segmentation, discussing the potential benefits and challenges of applying large foundation models to help with cancer diagnoses.

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用于癌症分类的大型基础模型
最近,大型语言模型(如 ChatGPT)在理解和生成类人文本方面取得了巨大进步,并在自然语言处理方面取得了相当大的成功。这些基础模型在计算机视觉方面也表现出色。然而,越来越多的人需要将这些技术用于特定的医疗任务,特别是用于识别图像中的癌症。本文探讨了如何将这些基础模型(如segment anything 模型)用于癌症分割,讨论了应用大型基础模型帮助癌症诊断的潜在优势和挑战。
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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来源期刊
CiteScore
4.40
自引率
0.00%
发文量
202
审稿时长
2 months
期刊介绍: Technology in Cancer Research & Treatment (TCRT) is a JCR-ranked, broad-spectrum, open access, peer-reviewed publication whose aim is to provide researchers and clinicians with a platform to share and discuss developments in the prevention, diagnosis, treatment, and monitoring of cancer.
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