Segmentation-based multi-scale attention model for KRAS mutation prediction in rectal cancer

Song, Kai; Zhao, Zijuan; Wang, Jiawen; Qiang, Yan; Zhao, Juanjuan; Zia, Muhammad Bilal

doi:10.1007/s13042-021-01447-w

Cited by 5 publications

(3 citation statements)

References 40 publications

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“…Wang S et al [47] firstly proposed an end-to-end deep learning model that uses CT images to predict the EGFR mutation status in lung adenocarcinoma. Song K et al [38] proposed a joint network named segmentation-based multi-scale attention model (SMSAM) to predict the mutation status of KRAS gene in rectal cancer. Qin R et al [29] proposed a hybrid network combining 3D CNN and RNN to design multi-type features and analyze their dependencies for the prediction of EGFR mutation status.…”

Section: Introductionmentioning

confidence: 99%

Residual neural network with mixed loss based on batch training technique for identification of EGFR mutation status in lung cancer

Jia

Hou

et al. 2023

Multimed Tools Appl

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Epidermal growth factor receptor (EGFR) is the key to targeted therapy with tyrosine kinase inhibitors in lung cancer. Traditional identification of EGFR mutation status requires biopsy and sequence testing, which may not be suitable for certain groups who cannot perform biopsy. In this paper, using easily accessible and non-invasive CT images, the residual neural network (ResNet) with mixed loss based on batch training technique is proposed for identification of EGFR mutation status in lung cancer. In this model, the ResNet is regarded as the baseline for feature extraction to avoid the gradient disappearance. Besides, a new mixed loss based on the batch similarity and the cross entropy is proposed to guide the network to better learn the model parameters. The proposed mixed loss utilizes the similarity among batch samples to evaluate the distribution of training data, which can reduce the similarity of different classes and the difference of the same classes. In the experiments, VGG16Net, DenseNet, ResNet18, ResNet34 and ResNet50 models with the mixed loss are trained on the public CT dataset with 155 patients including EGFR mutation status from TCIA. The trained networks are employed to the collected preoperative CT dataset with 56 patients from the cooperative hospital for validating the efficiency of the proposed models. Experimental results show that the proposed models are more appropriate and effective on the lung cancer dataset for identifying the EGFR mutation status. In these models, the ResNet34 with mixed loss is optimal (accuracy = 81.58%, AUC = 0.8861, sensitivity = 80.02%, specificity = 82.90%).

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Section: Introductionmentioning

confidence: 99%

Residual neural network with mixed loss based on batch training technique for identification of EGFR mutation status in lung cancer

Jia

Hou

et al. 2023

Multimed Tools Appl

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“…Although references [4][5][6] [7] have overcome the limitations of traditional methods in detecting KRAS gene mutation status, the accuracy of the models is low due to the inability to accurately locate the lesion area. In the studies presented in references [8] and [9], the attention mechanism has been effectively incorporated into deep learning. However, the articles choose to first locate the key regions and then input them into the network model, which relies on manual preprocessing of the dataset.…”

Section: Introductionmentioning

confidence: 99%

“…Incorporating attention mechanisms into deep learning has been shown to improve the performance of network models in tasks such as image segmentation and prediction. Many researchers have explored attention mechanisms, such as Zhu et al Song et al combined spatial attention and channel attention, enabling the model to simultaneously focus on specific regions and channels or features in the image [8]. Ma et al utilized a spatial-frequency dual-branch attention model to predict the KRAS gene mutation status in CRC [9].…”

Section: Introductionmentioning

confidence: 99%

A context-aggregated attention model for predicting KRAS mutation status in colorectal cancer

Gu,

Zhao,

et al. 2023

Preprint

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Purpose: Accurate identification of kratom sarcoma(KRAS) gene mutation status is of great significance for prognosis and treatment response in colorectal cancer. However, MRI images of colorectal cancer suffer from complex background, variable shape of the same type of lesion, and current network models do not conform to end-to-end learning, so accurate identification of KRAS gene mutation status is still challenging. Methods: In this research, we propose a Context-Aggregated Attention Model(CAAM) for predicting KRAS mutation status in colorectal cancer. The model's general framework comprises a segmentation framework and a classification framework, where segmentation aids in the classification process. Initially, the pre-processed MRI images are fed into the modified TransUNet to generate segmentation feature maps. These segmented feature maps are then combined with the preprocessed image to create a bootstrap feature map. The bootstrap feature map guides Mask-Guided Module(MGM) and the Contextual Aggregation Convolution module(CAC) to derive the final classification results. To train the network, we employ a combined loss function that incorporates both segmentation and classification losses. Results: We trained the network using a ten-fold cross-validation method and evaluated our model using the T2-weighted MRI dataset. The experiments demonstrated that our model achieved an accuracy of 87.56% on the test dataset, outperforming the results of the comparative experiments. Conclusion: The results indicate that our proposed context-aggregated attention model can provide new perspectives and approaches for the auxiliary treatment of colorectal cancer.

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Multi-view subspace enhanced representation of manifold regularization and low-rank tensor constraint

Liu

et al. 2022

Int. J. Mach. Learn. & Cyber.

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Segmentation-based multi-scale attention model for KRAS mutation prediction in rectal cancer

Cited by 5 publications

References 40 publications

Residual neural network with mixed loss based on batch training technique for identification of EGFR mutation status in lung cancer

Residual neural network with mixed loss based on batch training technique for identification of EGFR mutation status in lung cancer

A context-aggregated attention model for predicting KRAS mutation status in colorectal cancer

Multi-view subspace enhanced representation of manifold regularization and low-rank tensor constraint

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