Research graph
References from 3D ViT network with selective feature enhancement and cross-dimensional interaction for colorectal T-stage classification in CT images. Local targets link to admitted publications; unresolved targets remain external evidence.
Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries
10.3322/caac.21834 · 2024 · External reference
Unresolved reference
2017 · External reference
Preoperative radiotherapy combined with total mesorectal excision for resectable rectal cancer
10.1056/nejmoa010580 · 2001 · External reference
Support vector machines
10.1109/5254.708428 · 1998 · External reference
Unresolved reference
1998 · External reference
Autoencoders
2023 · External reference
Random forests
10.1023/a:1010933404324 · 2001 · External reference
Logistic regression
10.1161/circulationaha.106.682658 · 2008 · External reference
The generalized LASSO
10.1109/tnn.2003.809398 · 2004 · External reference
Contrast-enhanced computed tomography colonography in preoperative distinction between T1-T2 and T3-T4 staging of colon cancer
10.1016/j.acra.2013.01.008 · 2013 · External reference
Diagnostic accuracy of CT for local staging of colon cancer: a systematic review and meta-analysis
10.2214/ajr.15.15785 · 2016 · External reference
Automatic prediction of T2/T3 staging of rectal cancer based on radiomics and machine learning
10.1016/j.bdr.2022.100346 · 2022 · External reference
Deep-learning-based 3D super-resolution MRI radiomics model: superior predictive performance in preoperative T-staging of rectal cancer
10.1007/s00330-022-08952-8 · 2023 · External reference
Deep residual learning for image recognition
2016 · External reference
Rethinking the inception architecture for computer vision
2016 · External reference
Densely connected convolutional networks
2017 · External reference
Predicting tumor deposits in rectal cancer: a combined deep learning model using T2-MR imaging and clinical features
10.1186/s13244-023-01564-w · 2023 · External reference
Rectal cancer: toward fully automatic discrimination of T2 and T3 rectal cancers using deep convolutional neural network
10.1002/ima.22311 · 2019 · External reference
LRCTNet: a lightweight rectal cancer T-staging network based on knowledge distillation via a pretrained Swin Transformer
10.1016/j.bspc.2025.107696 · 2025 · External reference
Advances and challenges in treatment of locally advanced rectal cancer
10.1200/jco.2014.60.1054 · 2015 · External reference
Diagnostic accuracy of computed tomography for colon cancer staging: a systematic review
10.3109/00365521.2011.574732 · 2011 · External reference
Rectal cancer: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up
10.1093/annonc/mdx224 · 2017 · External reference
The kinetics human action video dataset
10.48550/arxiv.1705.06950 · 2017 · External reference
The mystery of the Z-score
10.12945/j.aorta.2016.16.014 · 2016 · External reference
Uniformer: unifying convolution and self-attention for visual recognition
10.1109/tpami.2023.3282631 · 2023 · External reference
Simam: a simple, parameter-free attention module for convolutional neural networks
2021 · External reference
MCA: multidimensional collaborative attention in deep convolutional neural networks for image recognition
10.1016/j.engappai.2023.107079 · 2023 · External reference
Decoupled weight decay regularization
2017 · External reference
Transunet: transformers make strong encoders for medical image segmentation
2021 · External reference
Swin-umamba: Mamba-based unet with imagenet-based pretraining
2024 · External reference
On large-batch training for deep learning: generalization gap and sharp minima
External reference
10.1038/s41592-020-01008-z · 2021 · Admitted local publication
CRCFound: a colorectal cancer CT image foundation model based on self‐supervised learning
10.1002/advs.202407339 · 2025 · External reference
DIOR-ViT: differential ordinal learning vision transformer for cancer classification in pathology images
10.1016/j.media.2025.103708 · 2025 · External reference
