Two-Stage Selective Ensemble of CNN via Deep Tree Training for Medical Image Classification

Yang, Yun; Hu, Yuanyuan; Zhang, Xingyi; Wang, Song

doi:10.1109/tcyb.2021.3061147

Cited by 46 publications

(24 citation statements)

References 63 publications

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“…In the process of experimental simulation and verification, the 10-fold cross-validation method was used for verification, and the classifications Accuracy, F-Measure and Recall were recorded respectively. Accuracy [12,42] is the evaluation standard used by most machine learning algorithms or deep learning algorithms, and is an important indicator to measure the quality of an algorithm [40]. Substantially, precision correlated samples indicates the percentage occupied in the retrieved sample, recall represents the correlated samples are retrieved relevant percentage of the total samples.…”

Section: Experiments Resultsmentioning

confidence: 99%

Effective multilayer hybrid classification approach for automatic bridge health assessment on large-scale uncertain data

Nan

Yang

2021

Journal of Industrial Information Integration

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Section: Experiments Resultsmentioning

confidence: 99%

Effective multilayer hybrid classification approach for automatic bridge health assessment on large-scale uncertain data

Nan

Yang

2021

Journal of Industrial Information Integration

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“…Furthermore, authors in [35] proposed bilinear attention networks (BAN) that find bilinear attention distributions to utilize given vision-language information seamlessly. At the same time, some researchers have also explored the application of ensemble learning and transfer learning in medical research and have achieved outstanding results [36] – [39] . At present, although these algorithms have achieved good results in different tasks, they rely too much on the way pathologists manually divide the decision-making boundary of pathological tissues [40] , [41] .…”

Section: Related Workmentioning

confidence: 99%

Using Multi-Scale Convolutional Neural Network Based on Multi-Instance Learning to Predict the Efficacy of Neoadjuvant Chemoradiotherapy for Rectal Cancer

Zhang

Duan

Guo

et al. 2022

IEEE J. Transl. Eng. Health Med.

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Background: At present, radical total mesorectal excision after neoadjuvant chemoradiotherapy is crucial for locally advanced rectal cancer. Therefore, the use of histopathological images analysis technology to predict the efficacy of neoadjuvant chemoradiotherapy for rectal cancer is of great significance for the subsequent treatment of patients. Methods: In this study, we propose a new pathological images analysis method based on multi-instance learning to predict the efficacy of neoadjuvant chemoradiotherapy for rectal cancer. Specifically, we proposed a gated attention normalization mechanism based on the multilayer perceptron, which accelerates the convergence of stochastic gradient descent optimization and can speed up the training process. We also proposed a bilinear attention multi-scale feature fusion mechanism, which organically fuses the global features of the larger receptive fields and the detailed features of the smaller receptive fields and alleviates the problem of pathological images context information loss caused by block sampling. At the same time, we also designed a weighted loss function to alleviate the problem of imbalance between cancerous instances and normal instances. Results: We evaluated our method on a locally advanced rectal cancer dataset containing 150 whole slide images. In addition, to verify our method's generalization performance, we also tested on two publicly available datasets, Camelyon16 and MSKCC. The results show that the AUC values of our method on the Camelyon16 and MSKCC datasets reach 0.9337 and 0.9091, respectively. Conclusion: Our method has outstanding performance and advantages in predicting the efficacy of neoadjuvant chemoradiotherapy for rectal cancer.

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“…Recent studies showed that the most successful and accurate MIC pipelines are also heavily based on ensemble learning strategies [6]- [13]. In the machine learning field, the aim is to find a suitable hypothesis that maximizes prediction correctness.…”

Section: Introductionmentioning

confidence: 99%

An Analysis on Ensemble Learning Optimized Medical Image Classification With Deep Convolutional Neural Networks

Müller

Soto-Rey

2022

IEEE Access

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Novel and high-performance medical image classification pipelines are heavily utilizing ensemble learning strategies. The idea of ensemble learning is to assemble diverse models or multiple predictions and, thus, boost prediction performance. However, it is still an open question to what extent as well as which ensemble learning strategies are beneficial in deep learning based medical image classification pipelines. In this work, we proposed a reproducible medical image classification pipeline for analyzing the performance impact of the following ensemble learning techniques: Augmenting, Stacking, and Bagging. The pipeline consists of state-of-the-art preprocessing and image augmentation methods as well as 9 deep convolution neural network architectures. It was applied on four popular medical imaging datasets with varying complexity. Furthermore, 12 pooling functions for combining multiple predictions were analyzed, ranging from simple statistical functions like unweighted averaging up to more complex learning-based functions like support vector machines. Our results revealed that Stacking achieved the largest performance gain of up to 13% F1-score increase. Augmenting showed consistent improvement capabilities by up to 4% and is also applicable to single model based pipelines. Cross-validation based Bagging demonstrated significant performance gain close to Stacking, which resulted in an F1-score increase up to +11%. Furthermore, we demonstrated that simple statistical pooling functions are equal or often even better than more complex pooling functions. We concluded that the integration of ensemble learning techniques is a powerful method for any medical image classification pipeline to improve robustness and boost performance.

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Two-Stage Selective Ensemble of CNN via Deep Tree Training for Medical Image Classification

Cited by 46 publications

References 63 publications

Effective multilayer hybrid classification approach for automatic bridge health assessment on large-scale uncertain data

Effective multilayer hybrid classification approach for automatic bridge health assessment on large-scale uncertain data

Using Multi-Scale Convolutional Neural Network Based on Multi-Instance Learning to Predict the Efficacy of Neoadjuvant Chemoradiotherapy for Rectal Cancer

An Analysis on Ensemble Learning Optimized Medical Image Classification With Deep Convolutional Neural Networks

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