A vision transformer‐based deep transfer learning nomogram for predicting lymph node metastasis in lung adenocarcinoma

This study developed a new DTL model based on ViT to predict LNM status in lung adenocarcinoma patients and revealed that the performance of the ViT-based DTL model was comparable to that of classical CNN models, confirming that ViT was viable for deep learning tasks involving medical images. The ViT-based DTLN performed exceptionally well and can assist clinicians and radiologists in making accurate judgments and formulating appropriate treatment plans.

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Publication Details

Journal
Medical Physics
Published
2024-09-28
DOI
https://doi.org/10.1002/mp.17414
Citations
6
Primary Topic
Lung Cancer Diagnosis and Treatment
Type
article
Field-Weighted Citation Impact
2.51
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article

A vision transformer‐based deep transfer learning nomogram for predicting lymph node metastasis in lung adenocarcinoma

Shuya Yuan, Qiuyang Hou, Yi Luo, Chuan‐Yu Chen et al.
6 citations
Medical Physics
Lung Cancer Diagnosis and Treatment
2.51
article

A vision transformer‐based deep transfer learning nomogram for predicting lymph node metastasis in lung adenocarcinoma

Shuya Yuan, Qiuyang Hou, Yi Luo, Chuan‐Yu Chen, Kexue Deng, Jun Qiu
article en
6 citations

Abstract

This study developed a new DTL model based on ViT to predict LNM status in lung adenocarcinoma patients and revealed that the performance of the ViT-based DTL model was comparable to that of classical CNN models, confirming that ViT was viable for deep learning tasks involving medical images. The ViT-based DTLN performed exceptionally well and can assist clinicians and radiologists in making accurate judgments and formulating appropriate treatment plans.

Medical PhysicsVol. 52(1)
University of Science and Technology of China (CN)
Good health and well-being
Openalex Percentile: Top 10%
Lung Cancer Diagnosis and Treatment
2.51
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