A Deep Learning-Based Radiomics Model for Differentiating Benign and Malignant Renal Tumors

Zhou, Leilei; Zhang, Zuoheng; Chen, Yu‐Chen; Zhao, Zhenyu; Xia, Yin; Jiang, Hongbing

doi:10.1016/j.tranon.2018.10.012

Cited by 110 publications

(61 citation statements)

References 35 publications

(42 reference statements)

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“…Precision ( Pre ): It indicates the proportion of processed samples that are correctly divided into positive samples [ 25 ].

where N TP represents the number of satellite cloud pictures that should be correctly classified; N FP represents the number of true correct classification after passing through the typhoon satellite cloud picture prediction model.…”

Section: Methodsmentioning

confidence: 99%

Classification and Prediction of Typhoon Levels by Satellite Cloud Pictures through GC–LSTM Deep Learning Model

Zhou

Xiang

Huang

2020

Sensors

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Typhoons are some of the most serious natural disasters, and the key to disaster prevention and mitigation is typhoon level classification. How to better use data of satellite cloud pictures to achieve accurate classification of typhoon levels has become one of classification the hot issues in current studies. A new framework of deep learning neural network, Graph Convolutional–Long Short-Term Memory Network (GC–LSTM), is proposed, which is based on the data of satellite cloud pictures of the Himawari-8 satellite in 2010–2019. The Graph Convolutional Network (GCN) is used to process the irregular spatial structure of satellite cloud pictures effectively, and the Long Short-Term Memory (LSTM) network is utilized to learn the characteristics of satellite cloud pictures over time. Moreover, to verify the effectiveness and accuracy of the model, the prediction effect and model stability are compared with other models. The results show that: the algorithm performance of this model is better than other prediction models; the prediction accuracy rate of typhoon level classification reaches 92.35%, and the prediction accuracy of typhoons and super typhoons reaches 95.12%. The model can accurately identify typhoon eye and spiral cloud belt, and the prediction results are always kept in the minimum range compared with the actual results, which proves that the GC–LSTM model has stronger stability. The model can accurately identify the levels of different typhoons according to the satellite cloud pictures. In summary, the results can provide a theoretical basis for the related research of typhoon level classification.

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“…Precision ( Pre ): It indicates the proportion of processed samples that are correctly divided into positive samples [ 25 ].

Section: Methodsmentioning

confidence: 99%

Classification and Prediction of Typhoon Levels by Satellite Cloud Pictures through GC–LSTM Deep Learning Model

Zhou

Xiang

Huang

2020

Sensors

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“…We used our multichannel image representation described in a previous study [24] that could capture both the anatomical and metabolic characteristics of the lesion in a single three-channel image. The three-channel image representation naturally fit the input architecture of the CNNs.…”

Section: ) Three-channel Representationmentioning

confidence: 99%

“…This approach was chosen on account of the small size of the dataset. We used our subset by partitioning as previously described in [24] to prevent contamination of the training, validation and testing sets with images of the same patient. The results were reported in terms of accuracy (ACC), sensitivity (SEN), specificity (SPE), Matthews correlation coefficient (MCC) and AUC.…”

Section: F Performance Evaluationmentioning

confidence: 99%

Transfer Learning-Based DCE-MRI Method for Identifying Differentiation Between Benign and Malignant Breast Tumors

et al. 2020

Self Cite

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In this paper, we propose a transfer learning-based dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) method for classifying fibroadenoma and invasive ductal carcinoma (IDC) in breast tumors. A total of 207 breast tumors from patients were collected and identified by pathologic diagnosis within 15 days after enhanced DCE-MRI examination; 119 patients (average age 50.52±10.33 years) had fibroadenomas, and 88 patients (average age 42.20±10.10 years) had IDCs. Two lesion-level models were built based on the InceptionV3 and VGG19 models, which were pretrained with the ImageNet dataset. The effects of different depths of transfer learning were examined. The network's performance was evaluated through five-fold cross validation. In the lesion-level models, the model based on Inception V3 obtained better results (area under the receiver operating characteristic curve (AUC) = 0.89) when the weights were frozen before layer-276. The other model based on VGG19 obtained better results (AUC = 0.87) when the weights were frozen before layer-13. Compared with the image-level models, both lesion-level models displayed better discrimination (accuracy increased by 13% and 14%) based on the VGG19 and Inception V3 models, respectively. Our research confirms that transfer learning can be utilized to classify fibroadenomas and IDCs in DCE-MRI images. Different depths of transfer learning result in different performances, and our proposed lesion-level model notably improves the classification accuracy. INDEX TERMS Magnetic resonance imaging, invasive ductal carcinoma, fibroadenoma, transfer learning, lesion classification.

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“…Besides this, the visual geometry group (VGG) network [ 5 ] is utilized to identity papillary thyroid carcinomas in cytological images [ 6 ] and discover COVID-19 cases based on X-ray images [ 7 ]. In addition, the Inception-V3 [ 8 ] is trained for distinguishing skin cancer images from normal ones [ 9 ], and differentiating benign and malignant renal tumors based on CT images [ 10 ]. Moreover, the residual network (ResNet) [ 11 ] is applied to HEp-2 cell classification [ 12 ] and the quality assessment of retinal OCT images [ 13 ].…”

Section: Introductionmentioning

confidence: 99%

Batch Similarity Based Triplet Loss Assembled into Light-Weighted Convolutional Neural Networks for Medical Image Classification

Huang

Zhou

Zhu

et al. 2021

Sensors

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In many medical image classification tasks, there is insufficient image data for deep convolutional neural networks (CNNs) to overcome the over-fitting problem. The light-weighted CNNs are easy to train but they usually have relatively poor classification performance. To improve the classification ability of light-weighted CNN models, we have proposed a novel batch similarity-based triplet loss to guide the CNNs to learn the weights. The proposed loss utilizes the similarity among multiple samples in the input batches to evaluate the distribution of training data. Reducing the proposed loss can increase the similarity among images of the same category and reduce the similarity among images of different categories. Besides this, it can be easily assembled into regular CNNs. To appreciate the performance of the proposed loss, some experiments have been done on chest X-ray images and skin rash images to compare it with several losses based on such popular light-weighted CNN models as EfficientNet, MobileNet, ShuffleNet and PeleeNet. The results demonstrate the applicability and effectiveness of our method in terms of classification accuracy, sensitivity and specificity.

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A Deep Learning-Based Radiomics Model for Differentiating Benign and Malignant Renal Tumors

Cited by 110 publications

References 35 publications

Classification and Prediction of Typhoon Levels by Satellite Cloud Pictures through GC–LSTM Deep Learning Model

Classification and Prediction of Typhoon Levels by Satellite Cloud Pictures through GC–LSTM Deep Learning Model

Transfer Learning-Based DCE-MRI Method for Identifying Differentiation Between Benign and Malignant Breast Tumors

Batch Similarity Based Triplet Loss Assembled into Light-Weighted Convolutional Neural Networks for Medical Image Classification

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