Deep Convolutional Neural Networks With Ensemble Learning and Generative Adversarial Networks for Alzheimer’s Disease Image Data Classification

Logan, Robert L.; Williams, Brian; Silva, Maria Ferreira da; Indani, Akash; Schcolnicov, Nicolas; Ganguly, Anjali; Miller, Sean

doi:10.3389/fnagi.2021.720226

Cited by 39 publications

(20 citation statements)

References 98 publications

(111 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For example, ensemble techniques are used to teach a model how to average or combine predictions of multiple models. These steps are mostly intransparent and hardly reproducible too [28,6,4,29]. Reproducibility is essential also in the context of fault tolerance, iterative refinement, debugging and optimization of adaptable models, especially for large scale and distributed workflow applications, like cloud computing platforms and Industry 4.0 [32].…”

Section: Discussionmentioning

confidence: 99%

Deep Learning Reproducibility and Explainable AI (XAI)

Leventi-Peetz¹,

T.²

2022

Preprint

View full text Add to dashboard Cite

The nondeterminism of Deep Learning (DL) training algorithms and its influence on the explainability of neural network (NN) models are investigated in this work with the help of image classification examples. To discuss the issue, two convolutional neural networks (CNN) have been trained and their results compared. The comparison serves the exploration of the feasibility of creating deterministic, robust DL models and deterministic explainable artificial intelligence (XAI) in practice. Successes and limitation of all here carried out efforts are described in detail. The source code of the attained deterministic models has been listed in this work. Reproducibility is indexed as a development-phase-component of the Model Governance Framework, proposed by the EU within their excellence in AI approach. Furthermore, reproducibility is a requirement for establishing causality for the interpretation of model results and building of trust towards the overwhelming expansion of AI systems applications. Problems that have to be solved on the way to reproducibility and ways to deal with some of them, are examined in this work.

show abstract

Section: Discussionmentioning

confidence: 99%

Deep Learning Reproducibility and Explainable AI (XAI)

Leventi-Peetz¹,

T.²

2022

Preprint

View full text Add to dashboard Cite

show abstract

“…During testing on the hold-out test set, model ensembling was performed, averaging predictions obtained from the three models trained and validated during 3-fold cross validation. Ensemble learning combines the predictions from multiple deep learning models to reduce the variance of predictions and reduce generalization error Logan et al (2021).…”

Section: Single Slice Probability Predictionmentioning

confidence: 99%

PO-1606 Slice-by-slice deep learning aided oropharyngeal cancer segmentation on PET and CT images

Biase¹,

Sijtsema²,

Langendijk³

et al. 2022

Radiotherapy and Oncology

View full text Add to dashboard Cite

“…Some recent reviews reported the application of GAN in AD predictions and image classification. Logan et al (2021) reported the application value of GAN in improving image quality and converting the modality. However, only two studies were included, and the results for the AD diagnosis were not reported.…”

Section: Introductionmentioning

confidence: 99%

Diagnostic Performance of Generative Adversarial Network-Based Deep Learning Methods for Alzheimer’s Disease: A Systematic Review and Meta-Analysis

Zou

et al. 2022

Front. Aging Neurosci.

View full text Add to dashboard Cite

Alzheimer’s disease (AD) is the most common form of dementia. Currently, only symptomatic management is available, and early diagnosis and intervention are crucial for AD treatment. As a recent deep learning strategy, generative adversarial networks (GANs) are expected to benefit AD diagnosis, but their performance remains to be verified. This study provided a systematic review on the application of the GAN-based deep learning method in the diagnosis of AD and conducted a meta-analysis to evaluate its diagnostic performance. A search of the following electronic databases was performed by two researchers independently in August 2021: MEDLINE (PubMed), Cochrane Library, EMBASE, and Web of Science. The Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool was applied to assess the quality of the included studies. The accuracy of the model applied in the diagnosis of AD was determined by calculating odds ratios (ORs) with 95% confidence intervals (CIs). A bivariate random-effects model was used to calculate the pooled sensitivity and specificity with their 95% CIs. Fourteen studies were included, 11 of which were included in the meta-analysis. The overall quality of the included studies was high according to the QUADAS-2 assessment. For the AD vs. cognitively normal (CN) classification, the GAN-based deep learning method exhibited better performance than the non-GAN method, with significantly higher accuracy (OR 1.425, 95% CI: 1.150–1.766, P = 0.001), pooled sensitivity (0.88 vs. 0.83), pooled specificity (0.93 vs. 0.89), and area under the curve (AUC) of the summary receiver operating characteristic curve (SROC) (0.96 vs. 0.93). For the progressing MCI (pMCI) vs. stable MCI (sMCI) classification, the GAN method exhibited no significant increase in the accuracy (OR 1.149, 95% CI: 0.878–1.505, P = 0.310) or the pooled sensitivity (0.66 vs. 0.66). The pooled specificity and AUC of the SROC in the GAN group were slightly higher than those in the non-GAN group (0.81 vs. 0.78 and 0.81 vs. 0.80, respectively). The present results suggested that the GAN-based deep learning method performed well in the task of AD vs. CN classification. However, the diagnostic performance of GAN in the task of pMCI vs. sMCI classification needs to be improved.Systematic Review Registration: [PROSPERO], Identifier: [CRD42021275294].

show abstract

Deep Convolutional Neural Networks With Ensemble Learning and Generative Adversarial Networks for Alzheimer’s Disease Image Data Classification

Cited by 39 publications

References 98 publications

Deep Learning Reproducibility and Explainable AI (XAI)

Deep Learning Reproducibility and Explainable AI (XAI)

PO-1606 Slice-by-slice deep learning aided oropharyngeal cancer segmentation on PET and CT images

Diagnostic Performance of Generative Adversarial Network-Based Deep Learning Methods for Alzheimer’s Disease: A Systematic Review and Meta-Analysis

Contact Info

Product

Resources

About