Shifted window-based Transformer with multimodal representation for the systematic staging of rectal cancer

Systematic staging of rectal cancer aims to determine tumor invasion degree and lymph node metastasis (LNM) status. Artificial intelligence technologies can aid physicians in making more accurate therapeutic decisions. Current research on rectal cancer segmentation primarily relies on convolutional...

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Bibliographic Details
Published in:Service oriented computing and applications Vol. 19; no. 3; pp. 225 - 236
Main Authors: Wang, Haoyu, Li, Peihong
Format: Journal Article
Language:English
Published: London Springer London 01.09.2025
Springer Nature B.V
Subjects:
Artificial intelligence
Artificial neural networks
Cancer
Classification
Clinical medicine
Colorectal cancer
Computer Appl. in Administrative Data Processing
Computer Science
Computer Systems Organization and Communication Networks
Datasets
Diagnosis
e-Commerce/e-business
Efficiency
IT in Business
Lymphatic system
Management of Computing and Information Systems
Medical diagnosis
Metastasis
Neural networks
Original Research Paper
Patients
Personal information
Physicians
Radiomics
Rectum
Regression analysis
Representations
Segmentation
Software Engineering/Programming and Operating Systems
Tumors
Lymph node metastasis
Multimodal representation
Rectal cancer
Swin Transformer
ISSN:1863-2386, 1863-2394
Online Access:Get full text
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Summary:Systematic staging of rectal cancer aims to determine tumor invasion degree and lymph node metastasis (LNM) status. Artificial intelligence technologies can aid physicians in making more accurate therapeutic decisions. Current research on rectal cancer segmentation primarily relies on convolutional neural networks. However, convolution operations’ limitations often result in ineffective capture of long-distance dependencies. Moreover, existing LNM diagnosis methods typically necessitate manual extraction of radiomics features from rectal cancer lesions. However, the efficacy of these features heavily depends on the specific dataset employed. In this paper, we propose a Transformer-based multi-modal rectal cancer diagnostic framework. This framework employs the hierarchical feature representation of the Swin Transformer to accurately segment tumors and adaptively extracts multi-scale features for LNM diagnosis. Compared to the current state-of-the-art models, our model has improved the accuracy of tumor segmentation and LNM classification by 3.62% and 4.10%, respectively.
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ISSN:1863-2386
1863-2394
DOI:10.1007/s11761-024-00400-3