GANDALF: Generative Adversarial Networks with Discriminator-Adaptive Loss Fine-Tuning for Alzheimer’s Disease Diagnosis from MRI

Shin, Hoo-Chang; Ihsani, Alvin; Xu, Ziyue; Mandava, Swetha; Sreenivas, Sharath Turuvekere; Forster, Christopher J.; Cha, Jiook; Initiative, Alzheimer’s Disease Neuroimaging

doi:10.1007/978-3-030-59713-9_66

Cited by 16 publications

(15 citation statements)

References 21 publications

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“…Two studies established a test set from the collection of clinical data ( Wegmayr et al, 2019 ; Kim et al, 2020 ). Regarding the data modality, 36 percent (5/14) of studies used data from two modalities ( Figure 2C ; Pan et al, 2018 ; Yan et al, 2018 ; Shin et al, 2020 ; Gao et al, 2021 ; Lin W. et al, 2021 ). One study used MRI and other clinical data (age, sex, education level, and other parameters) ( Zhao et al, 2021 ).…”

Section: Resultsmentioning

confidence: 99%

“…One study used MRI and other clinical data (age, sex, education level, and other parameters) ( Zhao et al, 2021 ). A deep convolutional GAN (DCGAN) was applied in 3 studies ( Islam and Zhang, 2020 ; Kang et al, 2021 ; Sajjad et al, 2021 ), and conditional GAN (CGAN) was applied in 2 studies ( Figure 2E ; Yan et al, 2018 ; Shin et al, 2020 ). The type of GAN in the remaining studies varied.…”

Section: Resultsmentioning

confidence: 99%

“…The type of GAN in the remaining studies varied. For the diagnostic task, 11 studies focused on the AD vs. CN classification ( Pan et al, 2018 ; Islam and Zhang, 2020 ; Kim et al, 2020 ; Shin et al, 2020 ; Baydargil et al, 2021 ; Gao et al, 2021 ; Han et al, 2021 ; Kang et al, 2021 ; Lin W. et al, 2021 ; Sajjad et al, 2021 ; Zhou X. et al, 2021 ), and 7 studies were devoted to the pMCI vs. sMCI classification ( Figure 2D ; Pan et al, 2018 ; Yan et al, 2018 ; Wegmayr et al, 2019 ; Gao et al, 2021 ; Kang et al, 2021 ; Lin W. et al, 2021 ; Zhao et al, 2021 ). For the assessment of the diagnostic performance, accuracy was reported in all studies, while sensitivity and specificity were reported in 6 studies examining the AD vs. CN classification ( Pan et al, 2018 ; Kim et al, 2020 ; Gao et al, 2021 ; Kang et al, 2021 ; Lin W. et al, 2021 ; Zhou X. et al, 2021 ) and 4 studies examining the pMCI vs. sMCI classification ( Pan et al, 2018 ; Gao et al, 2021 ; Kang et al, 2021 ; Lin W. et al, 2021 ).…”

Section: Resultsmentioning

confidence: 99%

“…Eleven studies focused on the application of GAN to the task of AD vs. CN classification ( Pan et al, 2018 ; Islam and Zhang, 2020 ; Kim et al, 2020 ; Shin et al, 2020 ; Baydargil et al, 2021 ; Gao et al, 2021 ; Han et al, 2021 ; Kang et al, 2021 ; Lin W. et al, 2021 ; Sajjad et al, 2021 ; Zhou X. et al, 2021 ). Meta-analyses were performed on 6 studies reporting the accuracy, sensitivity, and specificity ( Pan et al, 2018 ; Kim et al, 2020 ; Gao et al, 2021 ; Kang et al, 2021 ; Lin W. et al, 2021 ; Zhou X. et al, 2021 ).…”

Section: Resultsmentioning

confidence: 99%

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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.

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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].

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

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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.

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“…The literature describes several other applications of image-to-image translation in medicine and biology. Examples include enhancing medical [22] and histopathological [23] images to facilitate diagnosis. Others have applied similar approaches to images obtained from electron [24] and fluorescence [25–27] microscopy with the goal of detecting gold nanoparticles or subcellular components.…”

Section: Discussionmentioning

confidence: 99%

Prot2Prot: A Deep Learning Model for Rapid, Photorealistic Macromolecular Visualization

Durrant

2022

Preprint

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Motivation: Molecular visualization is a cornerstone of structural biology, providing insights into the form and function of biomolecules that are difficult to achieve any other way. Scientific analysis, publication, education, and outreach often benefit from photorealistic molecular depictions rendered using advanced computer-graphics programs such as Maya, 3ds Max, and Blender. However, setting up molecular scenes in these programs can be laborious even for expert users, and rendering often requires substantial time and computer resources. Results: We have created a deep-learning model called Prot2Prot that quickly imitates photorealistic visualization styles, given a much simpler, easy-to-generate molecular representation. The resulting images are often indistinguishable from images rendered using industry-standard 3D graphics programs, but they can be created in a fraction of the time, even when running in a web browser. To the best of our knowledge, Prot2Prot is the first example of image-to-image translation applied to macromolecular visualization. Availability: Prot2Prot is available free of charge, released under the terms of the Apache License, Version 2.0. Users can access a Prot2Prot-powered web app without registration at http://durrantlab.com/prot2prot.

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Recent advances of Transformers in medical image analysis: A comprehensive review

Xia

Wang

2023

MedComm – Future Medicine

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Recent works have shown that Transformer's excellent performances on natural language processing tasks can be maintained on natural image analysis tasks. However, the complicated clinical settings in medical image analysis and varied disease properties bring new challenges for the use of Transformer. The computer vision and medical engineering communities have devoted significant effort to medical image analysis research based on Transformer with especial focus on scenario-specific architectural variations.In this paper, we comprehensively review this rapidly developing area by covering the latest advances of Transformer-based methods in medical image analysis of different settings. We first give introduction of basic mechanisms of Transformer including implementations of selfattention and typical architectures. The important research problems in various medical image data modalities, clinical visual tasks, organs and diseases are then reviewed systemically. We carefully collect 276 very recent works and 76 public medical image analysis datasets in an organized structure. Finally, discussions on open problems and future research directions are also provided. We expect this review to be an up-to-date roadmap and serve as a reference source in pursuit of boosting the development of medical image analysis field.

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GANDALF: Generative Adversarial Networks with Discriminator-Adaptive Loss Fine-Tuning for Alzheimer’s Disease Diagnosis from MRI

Cited by 16 publications

References 21 publications

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

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

Prot2Prot: A Deep Learning Model for Rapid, Photorealistic Macromolecular Visualization

Recent advances of Transformers in medical image analysis: A comprehensive review

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