High-throughput precision MRI assessment with integrated stack-ensemble deep learning can enhance the preoperative prediction of prostate cancer Gleason grade

Bao, Jie; Hou, Ying; Qin, Lang; Zhi, Rui; Wang, Ximing; Shi, Hai; Sun, Hongzan; Hu, Chunhong; Zhang, Yu-Dong

doi:10.1038/s41416-022-02134-5

Cited by 5 publications

(10 citation statements)

References 49 publications

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“…A number of recent studies of ensemble deep learning for biomedical imaging have advanced the field [ 34 , 35 ]. A case in point is Shokouhifar et al.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Another prime example is Bao et al. [ 35 ], who utilized integrated stack-ensemble deep learning to enhance the preoperative prediction of prostate cancer Gleason grade.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Numerous investigations have delved into the utilization of CNNs for the detection and categorization of skin cancer over the past two decades [ 7 , 32 , 35 , 36 ]. As an illustration, Esteva et al.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The max voting ensemble technique is a powerful approach within ensemble learning [ 8 , 34 , 35 ]. It aggregates predictions from multiple models and selects the class with the highest frequency of votes as the final prediction.…”

Section: Literature Reviewmentioning

confidence: 99%

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Combining State-of-the-Art Pre-Trained Deep Learning Models: A Noble Approach for Skin Cancer Detection Using Max Voting Ensemble

Hossain,

Arefin

et al. 2023

Diagnostics

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Skin cancer poses a significant healthcare challenge, requiring precise and prompt diagnosis for effective treatment. While recent advances in deep learning have dramatically improved medical image analysis, including skin cancer classification, ensemble methods offer a pathway for further enhancing diagnostic accuracy. This study introduces a cutting-edge approach employing the Max Voting Ensemble Technique for robust skin cancer classification on ISIC 2018: Task 1-2 dataset. We incorporate a range of cutting-edge, pre-trained deep neural networks, including MobileNetV2, AlexNet, VGG16, ResNet50, DenseNet201, DenseNet121, InceptionV3, ResNet50V2, InceptionResNetV2, and Xception. These models have been extensively trained on skin cancer datasets, achieving individual accuracies ranging from 77.20% to 91.90%. Our method leverages the synergistic capabilities of these models by combining their complementary features to elevate classification performance further. In our approach, input images undergo preprocessing for model compatibility. The ensemble integrates the pre-trained models with their architectures and weights preserved. For each skin lesion image under examination, every model produces a prediction. These are subsequently aggregated using the max voting ensemble technique to yield the final classification, with the majority-voted class serving as the conclusive prediction. Through comprehensive testing on a diverse dataset, our ensemble outperformed individual models, attaining an accuracy of 93.18% and an AUC score of 0.9320, thus demonstrating superior diagnostic reliability and accuracy. We evaluated the effectiveness of our proposed method on the HAM10000 dataset to ensure its generalizability. Our ensemble method delivers a robust, reliable, and effective tool for the classification of skin cancer. By utilizing the power of advanced deep neural networks, we aim to assist healthcare professionals in achieving timely and accurate diagnoses, ultimately reducing mortality rates and enhancing patient outcomes.

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“…A number of recent studies of ensemble deep learning for biomedical imaging have advanced the field [ 34 , 35 ]. A case in point is Shokouhifar et al.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Another prime example is Bao et al. [ 35 ], who utilized integrated stack-ensemble deep learning to enhance the preoperative prediction of prostate cancer Gleason grade.…”

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

See 2 more Smart Citations

Combining State-of-the-Art Pre-Trained Deep Learning Models: A Noble Approach for Skin Cancer Detection Using Max Voting Ensemble

Hossain,

Arefin

et al. 2023

Diagnostics

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“…Advance identification of these patients before treatment may be beneficial to their prognoses. To address these problems, many studies have constructed a variety of AI models for the diagnoses and treatments of PCa, such as the diagnosis of csPCa [ 54 , 55 ], prediction of Gleason grade [ 56 ], prediction of biochemical recurrence (BCR) [ 57 ], and extracapsular extension (ECE) [ 58 ]. They have compared the performances of these models with those of visual assessments based on PI-RADS or other clinical assessments.…”

Section: Introductionmentioning

confidence: 99%

What benefit can be obtained from magnetic resonance imaging diagnosis with artificial intelligence in prostate cancer compared with clinical assessments?

et al. 2023

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The present study aimed to explore the potential of artificial intelligence (AI) methodology based on magnetic resonance (MR) images to aid in the management of prostate cancer (PCa). To this end, we reviewed and summarized the studies comparing the diagnostic and predictive performance for PCa between AI and common clinical assessment methods based on MR images and/or clinical characteristics, thereby investigating whether AI methods are generally superior to common clinical assessment methods for the diagnosis and prediction fields of PCa. First, we found that, in the included studies of the present study, AI methods were generally equal to or better than the clinical assessment methods for the risk assessment of PCa, such as risk stratification of prostate lesions and the prediction of therapeutic outcomes or PCa progression. In particular, for the diagnosis of clinically significant PCa, the AI methods achieved a higher summary receiver operator characteristic curve (SROC-AUC) than that of the clinical assessment methods (0.87 vs. 0.82). For the prediction of adverse pathology, the AI methods also achieved a higher SROC-AUC than that of the clinical assessment methods (0.86 vs. 0.75). Second, as revealed by the radiomics quality score (RQS), the studies included in the present study presented a relatively high total average RQS of 15.2 (11.0–20.0). Further, the scores of the individual RQS elements implied that the AI models in these studies were constructed with relatively perfect and standard radiomics processes, but the exact generalizability and clinical practicality of the AI models should be further validated using higher levels of evidence, such as prospective studies and open-testing datasets.

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PFCA-Net: a post-fusion based cross-attention model for predicting PCa Gleason Group using multiparametric MRI

Xinyu,

Yan,

Yin

et al. 2023

2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

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High-throughput precision MRI assessment with integrated stack-ensemble deep learning can enhance the preoperative prediction of prostate cancer Gleason grade

Cited by 5 publications

References 49 publications

Combining State-of-the-Art Pre-Trained Deep Learning Models: A Noble Approach for Skin Cancer Detection Using Max Voting Ensemble

Combining State-of-the-Art Pre-Trained Deep Learning Models: A Noble Approach for Skin Cancer Detection Using Max Voting Ensemble

What benefit can be obtained from magnetic resonance imaging diagnosis with artificial intelligence in prostate cancer compared with clinical assessments?

PFCA-Net: a post-fusion based cross-attention model for predicting PCa Gleason Group using multiparametric MRI

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