Machine Learning for Neurodegenerative Disorder Diagnosis — Survey of Practices and Launch of Benchmark Dataset

Tagaris, Athanasios; Kollias, Dimitrios; Stafylopatis, Andreas; Tagaris, G.; Kollias, Stefanos

doi:10.1142/s0218213018500112

Cited by 42 publications

(80 citation statements)

References 49 publications

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“…Deep neural networks, including Convolutional (CNNs), Convolutional and Recurrent (CNN-RNNs) have been developed in [10,23] for PD prediction using the DaTscan and MRI data included in the above-mentioned database [11].…”

Section: Related Workmentioning

confidence: 99%

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Unified deep learning approach for prediction of Parkinson's disease

et al. 2020

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The paper presents a novel approach, based on deep learning, for diagnosis of Parkinson's disease through medical imaging. The approach includes analysis and use of the knowledge extracted by Deep Convolutional and Recurrent Neural Networks (DNNs) when trained with medical images, such as Magnetic Resonance Images and DaTscans. Internal representations of the trained DNNs constitute the extracted knowledge which is used in a transfer learning and domain adaptation manner, so as to create a unified framework for prediction of Parkinson's across different medical environments. A large experimental study is presented illustrating the ability of the proposed approach to effectively predict Parkinson's, using different medical image sets from real environments.

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Section: Related Workmentioning

confidence: 99%

“…At first, we extract appropriate internal features, say features v, from the DNN model trained with the dataset developed in [11].…”

Section: Introductionmentioning

confidence: 99%

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Unified deep learning approach for prediction of Parkinson's disease

et al. 2020

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“…We can do this, by expressing E P1 in Eq. (7) in terms of the following constraint: y (j) = d(j); j = 1, . .…”

Section: B Retraining Of Deep Neural Network With Annotated Latentmentioning

confidence: 99%

“…Both methods were based on the Alzheimer's disease neuroimaging initiative dataset, including medical images and assessments of several hundred subjects. Recently, CNNs and convolutional-recurrent neural network (CNN-RNN) architectures have been developed for prediction of Parkinson's disease [6], based on a new database including Magnetic Resonance Imaging (MRI) data and Dopamine Transporters (DaT) Scans from patients with Parkinson's and non patients [7].…”

Section: Introductionmentioning

confidence: 99%

Predicting Parkinson’s Disease using Latent Information extracted from Deep Neural Networks

Kollia

Stafylopatis

Kollias

2019

2019 International Joint Conference on Neural Networks (IJCNN)

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This paper presents a new method for medical diagnosis of neurodegenerative diseases, such as Parkinson's, by extracting and using latent information from trained Deep convolutional, or convolutional-recurrent Neural Networks (DNNs). In particular, our approach adopts a combination of transfer learning, k-means clustering and k-Nearest Neighbour classification of deep neural network learned representations to provide enriched prediction of the disease based on MRI and/or DaT Scan data. A new loss function is introduced and used in the training of the DNNs, so as to perform adaptation of the generated learned representations between data from different medical environments. Results are presented using a recently published database of Parkinson's related information, which was generated and evaluated in a hospital environment.Index Terms-latent variable information, deep convolutional and recurrent neural networks, transfer learning and domain adaptation, modified loss function, prediction, Parkinson's disease, MRI, DaT Scan data.

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Combination of Medical Imaging and Demographic Data for Parkinson’s Disease Diagnosis

Pereira¹,

Fonseca²,

Ferreira³

2020

IFIP Advances in Information and Communication Technology

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The identification of biomarkers to discriminate Parkinson's Disease from other motor diseases is crucial to provide suitable treatment to patients. This study proposes a novel approach for the classification of structural Magnetic Resonance Imaging (MRI), Dopamine Transporter scan data (DaTscan) and demographic information (age and gender) to differentiate PD patients, "Scans Without Evidence for Dopaminergic Deficit" (SWEDD) patients and healthy control subjects using Convolutional Neural Networks (CNN). In Control vs PD, the accuracy of the classifier increased by adding subject gender from 94.5% to 96.0%, while in PD vs SWEDD adding age lead to 88.7% accuracy using slices encompassing the basal ganglia. The CNN was not able to successfully discriminate SWEDD vs Control. Our results suggested that pattern changes in slices encompassing the basal ganglia and the mesencephalon are relevant biomarkers for PD suggesting that this approach may have the potential to aid in PD biomarkers detection.

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Machine Learning for Neurodegenerative Disorder Diagnosis — Survey of Practices and Launch of Benchmark Dataset

Cited by 42 publications

References 49 publications

Unified deep learning approach for prediction of Parkinson's disease

Unified deep learning approach for prediction of Parkinson's disease

Predicting Parkinson’s Disease using Latent Information extracted from Deep Neural Networks

Combination of Medical Imaging and Demographic Data for Parkinson’s Disease Diagnosis

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