Classification of Alzheimer Disease on Imaging Modalities with Deep CNNs Using Cross-Modal Transfer Learning

Aderghal, Karim; Khvostikov, Alexander; Крылов, А. В.; Benois-Pineau, Jenny; Afdel, Karim; Catheline, Gwenaelle

doi:10.1109/cbms.2018.00067

Cited by 105 publications

(61 citation statements)

References 18 publications

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“…The proposed method shows higher (better) classification accuracies ranging from 3% to 18%, compared to the previous methods developed by Varma [7], Yudong [8], Jha [5] and Zhang [9] on OASIS dataset. In addition, performance improvements ranging from 3%-15% are achieved compared to the methods proposed by Zhang [6], Liu [12], Gupta [10], Payan [11], Alexander [13], Aderghal [14], Aderghal [15] and Ortiz [16] on ADNI dataset. The overall improvement of the proposed method relative to other conventional methods is shown in Fig.…”

Section: Resultsmentioning

confidence: 97%

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Using Deep CNN with Data Permutation Scheme for Classification of Alzheimer's Disease in Structural Magnetic Resonance Imaging (sMRI)

Lee

Ellahi

Choi

2019

IEICE Trans. Inf. & Syst.

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In this paper, we propose a novel framework for structural magnetic resonance image (sMRI) classification of Alzheimer's disease (AD) with data combination, outlier removal, and entropy-based data selection using AlexNet. In order to overcome problems of conventional classical machine learning methods, the AlexNet classifier, with a deep learning architecture, was employed for training and classification. A data permutation scheme including slice integration, outlier removal, and entropy-based sMRI slice selection is proposed to utilize the benefits of AlexNet. Experimental results show that the proposed framework can effectively utilize the AlexNet with the proposed data permutation scheme by significantly improving overall classification accuracies for AD classification. The proposed method achieves 95.35% and 98.74% classification accuracies on the OASIS and ADNI datasets, respectively, for the binary classification of AD and Normal Control (NC), and also achieves 98.06% accuracy for the ternary classification of AD, NC, and Mild Cognitive Impairment (MCI) on the ADNI dataset. The proposed method can attain significantly improved accuracy of up to 18.15%, compared to previously developed methods.

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Section: Resultsmentioning

confidence: 97%

“…The multiple modalities (sMRI and DTI) were used for AD classification in [13], where the left and right lobes of the hippocampus are considered as an input of CNN base classifier. In [14], a cross-model transfer learning was proposed. The model is first trained on sMRI and fine-tuned on DTI modality.…”

Section: Related Workmentioning

confidence: 99%

Using Deep CNN with Data Permutation Scheme for Classification of Alzheimer's Disease in Structural Magnetic Resonance Imaging (sMRI)

Lee

Ellahi

Choi

2019

IEICE Trans. Inf. & Syst.

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“…e main factor may be that the CNN network structure is not well adapted to the image characteristics in the brain medical database built in this paper. Other deep learning algorithms such as reference [56][57][58][59][60] have classification accuracy of 90.3%, 91.2%, 92.3%, 92.3%, and 92.5%, respectively. It can be seen that the classification accuracy of these deep learning algorithms is above 90%, which also shows that the deep learning algorithm constructed by the subsequent use of brain medical image feature information can better utilize brain medical image information for classification.…”

Section: Classification Methodsmentioning

confidence: 99%

“…e authors declare that there are no conflicts of interest. Method type Classification accuracy (%) Zhang et al [55] 88.3 CNN 89.2 Suk et al [56] 90.3 Liu et al [57] 91.2 Liu et al [58] 92.3 Shen et al [59] 92.3 Aderghal et al [60] 92.5 Our 95. 1 12 Complexity…”

Section: Conflicts Of Interestmentioning

confidence: 99%

Medical Image Classification Algorithm Based on Weight Initialization‐Sliding Window Fusion Convolutional Neural Network

2019

Complexity

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Due to the complexity of medical images, traditional medical image classification methods have been unable to meet actual application needs. In recent years, the rapid development of deep learning theory has provided a technical approach for solving medical image classification tasks. However, deep learning has the following problems in medical image classification. First, it is impossible to construct a deep learning model hierarchy for medical image properties; second, the network initialization weights of deep learning models are not well optimized. Therefore, this paper starts from the perspective of network optimization and improves the nonlinear modeling ability of the network through optimization methods. A new network weight initialization method is proposed, which alleviates the problem that existing deep learning model initialization is limited by the type of the nonlinear unit adopted and increases the potential of the neural network to handle different visual tasks. Moreover, through an in-depth study of the multicolumn convolutional neural network framework, this paper finds that the number of features and the convolution kernel size at different levels of the convolutional neural network are different. In contrast, the proposed method can construct different convolutional neural network models that adapt better to the characteristics of the medical images of interest and thus can better train the resulting heterogeneous multicolumn convolutional neural networks. Finally, using the adaptive sliding window fusion mechanism proposed in this paper, both methods jointly complete the classification task of medical images. Based on the above ideas, this paper proposes a medical classification algorithm based on a weight initialization/sliding window fusion for multilevel convolutional neural networks. The methods proposed in this study were applied to breast mass, brain tumor tissue, and medical image database classification experiments. The results show that the proposed method not only achieves a higher average accuracy than that of traditional machine learning and other deep learning methods but also is more stable and more robust.

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“…In order to complete the proposed task, we followed the idea of using a data source as initialization for the framework, transferring some domain knowledge to the final training. This is a recent trend that has been applied to medical imaging processing for different purposes, such as cardiac structures segmentation [29], Alzheimer disease classification [30], radiological breast lesions classification [31] and even digital pathology classification/segmentation [32].…”

Section: Related Workmentioning

confidence: 99%

Weakly-Supervised Classification of HER2 Expression in Breast Cancer Haematoxylin and Eosin Stained Slides

et al. 2020

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Human epidermal growth factor receptor 2 (HER2) evaluation commonly requires immunohistochemistry (IHC) tests on breast cancer tissue, in addition to the standard haematoxylin and eosin (H&E) staining tests. Additional costs and time spent on further testing might be avoided if HER2 overexpression could be effectively inferred from H&E stained slides, as a preliminary indication of the IHC result. In this paper, we propose the first method that aims to achieve this goal. The proposed method is based on multiple instance learning (MIL), using a convolutional neural network (CNN) that separately processes H&E stained slide tiles and outputs an IHC label. This CNN is pretrained on IHC stained slide tiles but does not use these data during inference/testing. H&E tiles are extracted from invasive tumour areas segmented with the HASHI algorithm. The individual tile labels are then combined to obtain a single label for the whole slide. The network was trained on slides from the HER2 Scoring Contest dataset (HER2SC) and tested on two disjoint subsets of slides from the HER2SC database and the TCGA-TCIA-BRCA (BRCA) collection. The proposed method attained 83.3 % classification accuracy on the HER2SC test set and 53.8 % on the BRCA test set. Although further efforts should be devoted to achieving improved performance, the obtained results are promising, suggesting that it is possible to perform HER2 overexpression classification on H&E stained tissue slides.

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Classification of Alzheimer Disease on Imaging Modalities with Deep CNNs Using Cross-Modal Transfer Learning

Cited by 105 publications

References 18 publications

Using Deep CNN with Data Permutation Scheme for Classification of Alzheimer's Disease in Structural Magnetic Resonance Imaging (sMRI)

Using Deep CNN with Data Permutation Scheme for Classification of Alzheimer's Disease in Structural Magnetic Resonance Imaging (sMRI)

Medical Image Classification Algorithm Based on Weight Initialization‐Sliding Window Fusion Convolutional Neural Network

Weakly-Supervised Classification of HER2 Expression in Breast Cancer Haematoxylin and Eosin Stained Slides

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