Explainable 3D-CNN for Multiple Sclerosis Patients Stratification

Cruciani, Federica; Brusini, Lorenza; Zucchelli, Mauro; Pinheiro, Gustavo Retuci; Setti, Francesco; Galazzo, Ilaria Boscolo; Deriche, Rachid; Rittner, Letícia; Calabrese, Massimiliano; Menegaz, Gloria

doi:10.1007/978-3-030-68796-0_8

Cited by 3 publications

(2 citation statements)

References 32 publications

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“…Cruciani et al 52 demonstrate the usefulness of LRP to visualize relevant features on brain magnetic resonance imaging modality (MRI) for multiple sclerosis classification. It is observed that the LRP heat maps are sparse and not always as intuitive which is also illustrated by Fig.…”

Section: Discussionmentioning

confidence: 99%

White Box Methods for Explanations of Convolutional Neural Networks in Image Classification Tasks

Ayyar,

Benois-Pineau,

Zemmari

2021

Preprint

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In recent years, deep learning has become prevalent to solve applications from multiple domains. Convolutional Neural Networks (CNNs) particularly have demonstrated state of the art performance for the task of image classification. However, the decisions made by these networks are not transparent and cannot be directly interpreted by a human. Several approaches have been proposed to explain to understand the reasoning behind a prediction made by a network. In this paper, we propose a topology of grouping these methods based on their assumptions and implementations. We focus primarily on white box methods that leverage the information of the internal architecture of a network to explain its decision. Given the task of image classification and a trained CNN, this work aims to provide a comprehensive and detailed overview of a set of methods that can be used to create explanation maps for a particular image, that assign an importance score to each pixel of the image based on its contribution to the decision of the network. We also propose a further classification of the white box methods based on their implementations to enable better comparisons and help researchers find methods best suited for different scenarios.

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Section: Discussionmentioning

confidence: 99%

White Box Methods for Explanations of Convolutional Neural Networks in Image Classification Tasks

Ayyar,

Benois-Pineau,

Zemmari

2021

Preprint

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“…To evaluate the Grad-CAM maps, Selvaraju et al 30 conducted user surveys to find which maps and models they find reliable. Cruciani et al 53 demonstrated the usefulness of LRP to visualize relevant features on brain magnetic resonance imaging modality for multiple sclerosis classification. It is observed that the LRP heat maps are sparse and not always as intuitive, which is also illustrated by Fig.…”

Section: Human Perception-based Evaluationmentioning

confidence: 99%

Review of white box methods for explanations of convolutional neural networks in image classification tasks

2021

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In recent years, deep learning has become prevalent to solve applications from multiple domains. Convolutional neural networks (CNNs) particularly have demonstrated state-ofthe-art performance for the task of image classification. However, the decisions made by these networks are not transparent and cannot be directly interpreted by a human. Several approaches have been proposed to explain the reasoning behind a prediction made by a network. We propose a topology of grouping these methods based on their assumptions and implementations. We focus primarily on white box methods that leverage the information of the internal architecture of a network to explain its decision. Given the task of image classification and a trained CNN, our work aims to provide a comprehensive and detailed overview of a set of methods that can be used to create explanation maps for a particular image, which assign an importance score to each pixel of the image based on its contribution to the decision of the network. We also propose a further classification of the white box methods based on their implementations to enable better comparisons and help researchers find methods best suited for different scenarios.

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An Explainable AI model in the assessment of Multiple Sclerosis using clinical data and Brain MRI lesion texture features ^*

Nicolaou,

Pantzaris,

Loizou

et al. 2023

2023 IEEE EMBS International Conference on Biomedical and Health Informatics (BHI)

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Explainable 3D-CNN for Multiple Sclerosis Patients Stratification

Cited by 3 publications

References 32 publications

White Box Methods for Explanations of Convolutional Neural Networks in Image Classification Tasks

White Box Methods for Explanations of Convolutional Neural Networks in Image Classification Tasks

Review of white box methods for explanations of convolutional neural networks in image classification tasks

An Explainable AI model in the assessment of Multiple Sclerosis using clinical data and Brain MRI lesion texture features ^*

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Explainable 3D-CNN for Multiple Sclerosis Patients Stratification

Cited by 3 publications

References 32 publications

White Box Methods for Explanations of Convolutional Neural Networks in Image Classification Tasks

White Box Methods for Explanations of Convolutional Neural Networks in Image Classification Tasks

Review of white box methods for explanations of convolutional neural networks in image classification tasks

An Explainable AI model in the assessment of Multiple Sclerosis using clinical data and Brain MRI lesion texture features *

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Product

Resources

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An Explainable AI model in the assessment of Multiple Sclerosis using clinical data and Brain MRI lesion texture features ^*