TY - JOUR
T1 - Large Foundation Model for Cancer Segmentation
AU - Ren, Zeyu
AU - Zhang, Yudong
AU - Wang, Shuihua
N1 - Publisher Copyright:
© The Author(s) 2024.
PY - 2024/1/1
Y1 - 2024/1/1
N2 - 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.
AB - 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.
KW - deep learning
KW - foundation model
KW - image segmentation
KW - machine learning
KW - medical image analysis
KW - segment anything model (SAM)
UR - http://www.scopus.com/inward/record.url?scp=85199578157&partnerID=8YFLogxK
U2 - 10.1177/15330338241266205
DO - 10.1177/15330338241266205
M3 - Editorial
C2 - 39051534
AN - SCOPUS:85199578157
SN - 1533-0346
VL - 23
JO - Technology in Cancer Research and Treatment
JF - Technology in Cancer Research and Treatment
ER -
