Large Foundation Model for Cancer Segmentation

Zeyu Ren; Yudong Zhang; Shuihua Wang

doi:10.1177/15330338241266205

Large Foundation Model for Cancer Segmentation

Zeyu Ren^*
, Yudong Zhang
, Shuihua Wang

^*Corresponding author for this work

University of Leicester
Jilin Agricultural University
King Abdulaziz University
Xi'an Jiaotong-Liverpool University

Research output: Contribution to journal › Editorial

13 Citations (Scopus)

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.

Original language	English
Journal	Technology in Cancer Research and Treatment
Volume	23
DOIs	https://doi.org/10.1177/15330338241266205
Publication status	Published - 1 Jan 2024

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

deep learning
foundation model
image segmentation
machine learning
medical image analysis
segment anything model (SAM)

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10.1177/15330338241266205

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title = "Large Foundation Model for Cancer Segmentation",

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.",

keywords = "deep learning, foundation model, image segmentation, machine learning, medical image analysis, segment anything model (SAM)",

author = "Zeyu Ren and Yudong Zhang and Shuihua Wang",

note = "Publisher Copyright: {\textcopyright} The Author(s) 2024.",

year = "2024",

month = jan,

day = "1",

doi = "10.1177/15330338241266205",

language = "English",

volume = "23",

journal = "Technology in Cancer Research and Treatment",

issn = "1533-0346",

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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 - https://www.scopus.com/pages/publications/85199578157

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 -

Large Foundation Model for Cancer Segmentation

Abstract

UN SDGs

Keywords

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