Introducing Transfer Learning to 3D ResNet-18 for Alzheimer’s Disease Detection on MRI Images

Ghahnavieh, Amir Ebrahimi; Luo, Siwei; Chiong, Raymond

doi:10.1109/ivcnz51579.2020.9290616

Cited by 61 publications

(26 citation statements)

References 22 publications

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“…In addition, ResNet makes the training process faster and is more accurate in comparison with the equivalent neural networks. Since 2D CNNs are incapable of understanding the relationship among slices in a 3D MRI scan, researchers extended 2D CNNs to 3D and reported a remarkable performance in feature extraction 22 . Therefore, we implemented a modified 18‐layer ResNet‐18 with 3D convolution kernels to localise the facial landmark from patient's MRI.…”

Section: Methodsmentioning

confidence: 99%

Facial landmark‐guided surface matching for image‐to‐patient registration with an RGB‐D camera

Sun

Hosny

et al. 2022

Robotics Computer Surgery

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Background: Fiducial marker-based image-to-patient registration is the most common way in image-guided neurosurgery, which is labour-intensive, time consuming, invasive and error prone. Methods:We proposed a method of facial landmark-guided surface matching for image-to-patient registration using an RGB-D camera. Five facial landmarks are localised from preoperative magnetic resonance (MR) images using deep learning and RGB image using Adaboost with multi-scale block local binary patterns, respectively. The registration of two facial surface point clouds derived from MR images and RGB-D data is initialised by aligning these five landmarks and further refined by weighted iterative closest point algorithm.Results: Phantom experiment results show the target registration error is less than 3 mm when the distance from the camera to the phantom is less than 1000 mm. The registration takes less than 10 s. Conclusions:The proposed method is comparable to the state-of-the-arts in terms of the accuracy yet more time-saving and non-invasive.

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Section: Methodsmentioning

confidence: 99%

Facial landmark‐guided surface matching for image‐to‐patient registration with an RGB‐D camera

Sun

Hosny

et al. 2022

Robotics Computer Surgery

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“…• Ref. 13 : The pre-trained 3D Resnet50 18 is the backbone of this model. The authors first doubled the number of slices for each case using a 3D cubic interpolation method.…”

Section: Comparisonmentioning

confidence: 99%

Robust Automated Framework for COVID-19 Disease Identification from a Multicenter Dataset of Chest CT Scans

Khademi¹,

Heidarian²,

Afshar³

et al. 2021

Preprint

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The objective of this study is to develop a robust deep learning-based framework to distinguish COVID-19, Community-Acquired Pneumonia (CAP), and Normal cases based on chest CT scans, which are acquired in different imaging centers using various scanners, scanning protocols, and radiation doses. We showed that while our proposed model is trained on a relatively small dataset acquired from only one imaging center using a specific scanning protocol, the model performs well on heterogeneous test sets obtained by multiple scanners using different technical parameters. We also showed that the model can be updated via an unsupervised approach to cope with the data shift between the train and test sets and enhance the robustness of the model upon receiving a new external dataset from a different center. More specifically, we extracted the subset of the test images for which the model generated a confident prediction and used the extracted subset along with the training set to re-train and update the benchmark model (the model trained on the initial train set). Finally, we adopted an ensemble architecture to aggregate the predictions from multiple versions of the model. For initial training and development purposes, an in-house dataset of 171 COVID-19, 60 CAP, and 76 Normal cases was used, which contained volumetric CT scans acquired from one imaging center using a constant standard radiation dose scanning protocol. To evaluate the model, we collected four different test sets retrospectively to investigate the effects of the shifts in the data characteristics on the model's performance. Among the test cases, there were CT scans with similar characteristics as the train set as well as noisy low-dose and ultra-low dose CT scans. In addition, some test CT scans were obtained from patients with a history of cardiovascular diseases or surgeries. The entire test dataset used in this study contained 51 COVID-19, 28 CAP, and 51 Normal cases. Experimental results indicate that our proposed framework performs well on all test sets achieving the a total accuracy of 96.15% (95%CI : [91.25 − 98.74]), COVID-19 sensitivity of 96.08% (95%CI : [86.54 − 99.5]), CAP sensitivity of 92.86% (95%CI : [76.50 − 99.19]), Normal sensitivity of 98.04% (95%CI : [89.55 − 99.95]) while the confidence intervals are obtained using the significance level of 0.05. The obtained AUC values (One class vs Others) are 0.993 (95%CI : [0.977 − 1]), 0.989 (95%CI : [0.962 − 1]), and 0.990 (95%CI : [0.971 − 1]) for COVID-19, CAP, and Normal classes, respectively. The experimental results also demonstrate the capability of the proposed unsupervised enhancement approach to improve the performance and robustness of the model when being evaluated on varied external test sets.

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“…This study aimed to investigate: (1) whether applying the denoising and deblurring method to 18 F-FDG PET images improves the performance of AD classification, and (2) whether 18 F-FDG PET image cropping improves the performance of AD classification using a modified deep learning model from 3D-ResNet, which has been recently described as a powerful prediction model for 3D medical images [ 16 ]. We also investigated the explainability difference using a class activation map [ 17 ] between deep learning outputs of raw images and denoised or deblurred images.…”

Section: Introductionmentioning

confidence: 99%

Effect of Denoising and Deblurring 18F-Fluorodeoxyglucose Positron Emission Tomography Images on a Deep Learning Model’s Classification Performance for Alzheimer’s Disease

et al. 2022

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Alzheimer’s disease (AD) is the most common progressive neurodegenerative disease. 18F-fluorodeoxyglucose positron emission tomography (18F-FDG PET) is widely used to predict AD using a deep learning model. However, the effects of noise and blurring on 18F-FDG PET images were not considered. The performance of a classification model trained using raw, deblurred (by the fast total variation deblurring method), or denoised (by the median modified Wiener filter) 18F-FDG PET images without or with cropping around the limbic system area using a 3D deep convolutional neural network was investigated. The classification model trained using denoised whole-brain 18F-FDG PET images achieved classification performance (0.75/0.65/0.79/0.39 for sensitivity/specificity/F1-score/Matthews correlation coefficient (MCC), respectively) higher than that with raw and deblurred 18F-FDG PET images. The classification model trained using cropped raw 18F-FDG PET images achieved higher performance (0.78/0.63/0.81/0.40 for sensitivity/specificity/F1-score/MCC) than the whole-brain 18F-FDG PET images (0.72/0.32/0.71/0.10 for sensitivity/specificity/F1-score/MCC, respectively). The 18F-FDG PET image deblurring and cropping (0.89/0.67/0.88/0.57 for sensitivity/specificity/F1-score/MCC) procedures were the most helpful for improving performance. For this model, the right middle frontal, middle temporal, insula, and hippocampus areas were the most predictive of AD using the class activation map. Our findings demonstrate that 18F-FDG PET image preprocessing and cropping improves the explainability and potential clinical applicability of deep learning models.

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Introducing Transfer Learning to 3D ResNet-18 for Alzheimer’s Disease Detection on MRI Images

Cited by 61 publications

References 22 publications

Facial landmark‐guided surface matching for image‐to‐patient registration with an RGB‐D camera

Facial landmark‐guided surface matching for image‐to‐patient registration with an RGB‐D camera

Robust Automated Framework for COVID-19 Disease Identification from a Multicenter Dataset of Chest CT Scans

Effect of Denoising and Deblurring 18F-Fluorodeoxyglucose Positron Emission Tomography Images on a Deep Learning Model’s Classification Performance for Alzheimer’s Disease

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