Shearlets as feature extractor for semantic edge detection: the model-based and data-driven realm

Andrade-Loarca, Héctor; Kutyniok, Gitta; Öktem, Ozan

doi:10.1098/rspa.2019.0841

Cited by 7 publications

(4 citation statements)

References 36 publications

(125 reference statements)

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“…The main benefit of adding these skip connections in the network is that if any of the layers impair the performance of the architecture, it is skipped through regularisation. ResNet [16] is a type of network‐in‐network (NIN) architecture as it consists many residual units stacked together. The collection of these residual units forms a building block for the ResNet architecture.…”

Section: Proposed Methodologymentioning

confidence: 99%

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Review and analysis of deep neural network models for Alzheimer's disease classification using brain medical resonance imaging

Pallawi

Singh

2023

Cognitive Comp and Systems

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Alzheimer's disease is a type of progressive neurological disorder which is irreversible and the patient suffers from severe memory loss. This disease is the seventh largest cause of death across the globe. As yet there is no cure for this disease, the only way to control it is its early diagnosis. Deep Learning techniques are mostly preferred in classification tasks because of their high accuracy over a large dataset. The main focus of this paper is on fine‐tuning and evaluating the Deep Convolutional Networks for Alzheimer's disease classification. An empirical analysis of various deep learning‐based neural network models has been done. The architectures evaluation includes InceptionV3, ResNet with 50 layers and 101 layers and DenseNet with 169 layers. The dataset has been taken from Kaggle which is publicly available and comprises of four classes which represents the various stages of Alzheimer's disease. In our experiment, the accuracy of DenseNet consistently improved with the increase in the number of epochs resulting in a 99.94% testing accuracy score better than the rest of the architectures. Although the results obtained are satisfactory, but for future research, we can apply transfer learning on other deep models like Inception V4, AlexNet etc., to increase accuracy and decrease computational time. Also, in future we can work on other datasets like ADNI or OASIS and use Positron emitted tomography, diffusion tensor imaging neuroimages and their combinations for better result.

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Section: Proposed Methodologymentioning

confidence: 99%

Section: Inceptionv3mentioning

confidence: 99%

Review and analysis of deep neural network models for Alzheimer's disease classification using brain medical resonance imaging

Pallawi

Singh

2023

Cognitive Comp and Systems

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“…Quantitative analysis. As a quantitative measure of the accuracy of our results we use the F 1 score on the image gradient, as is done in [40,2] (see also the DICE metric [30]). The gradient (or edge) F 1 score is a measure of how well we have recovered the gradient image ∇f (i.e.…”

Section: 2mentioning

confidence: 99%

Bragg scattering tomography

Webber¹,

Miller²

2021

IPI

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Here we introduce a new forward model and imaging modality for Bragg Scattering Tomography (BST). The model we propose is based on an Xray portal scanner with linear detector collimation, currently being developed for use in airport baggage screening. The geometry under consideration leads us to a novel two-dimensional inverse problem, where we aim to reconstruct the Bragg scattering differential cross section function from its integrals over a set of symmetric C 2 curves in the plane. The integral transform which describes the forward problem in BST is a new type of Radon transform, which we introduce and denote as the Bragg transform. We provide new injectivity results for the Bragg transform here, and describe how the conditions of our theorems can be applied to assist in the machine design of the portal scanner. Further we provide an extension of our results to n-dimensions, where a generalization of the Bragg transform is introduced. Here we aim to reconstruct a real valued function on R n+1 from its integrals over n-dimensional surfaces of revolution of C 2 curves embedded in R n+1 . Injectivity proofs are provided also for the generalized Bragg transform.

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“…A shearlet system is composed of three operations: Dilation, Shearing, and Translation. Here, we briefly review the definition provided by Andrade-Loarca et al (2020).…”

Section: Shearlet Systemsmentioning

confidence: 99%

Federated Learning without Revealing the Decision Boundaries

Yousefzadeh

2021

Preprint

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We consider the recent privacy preserving methods that train the models not on original images, but on mixed images that look like noise and hard to trace back to the original images. We explain that those mixed images will be samples on the decision boundaries of the trained model, and although such methods successfully hide the contents of images from the entity in charge of federated learning, they provide crucial information to that entity about the decision boundaries of the trained model. Once the entity has exact samples on the decision boundaries of the model, they may use it for effective adversarial attacks on the model during training and/or afterwards. If we have to hide our images from that entity, how can we trust them with the decision boundaries of our model? As a remedy, we propose a method to encrypt the images, and have a decryption module hidden inside the model. The entity in charge of federated learning will only have access to a set of complex-valued coefficients, but the model will first decrypt the images and then put them through the convolutional layers. This way, the entity will not see the training images and they will not know the location of the decision boundaries of the model.

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Shearlets as feature extractor for semantic edge detection: the model-based and data-driven realm

Cited by 7 publications

References 36 publications

Review and analysis of deep neural network models for Alzheimer's disease classification using brain medical resonance imaging

Review and analysis of deep neural network models for Alzheimer's disease classification using brain medical resonance imaging

Bragg scattering tomography

Federated Learning without Revealing the Decision Boundaries

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