Using ProtoPNet for Interpretable Alzheimer’s Disease Classification

Mohammadjafari, Sanaz; Çevik, Mücahit; Thanabalasingam, Mathusan; Başar, Ayşe; Initiative, Alzheimer’s Disease Neuroimaging

doi:10.21428/594757db.fb59ce6c

Cited by 16 publications

(12 citation statements)

References 18 publications

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“…The authors in [ 142 ] proposed a prototypical part network (ProtoPNet) that can highlight the image regions used for decision-making and can explain the reasoning process for the classification target by comparing the representative patches of the test image with the prototypes learned from a large number of data. To date, several studies have tested the explanation model proposed in [ 142 ] that was able to highlight image regions used for decision making in medical imaging fields, such as for mass lesion classification [ 143 ], lung disease detection [ 144 , 145 ], and Alzheimer’s diseases classification [ 146 ]. Future research in the brain tumor classification field will need to test how explainable models influence the attitudes and decision-making processes of radiologists or other clinicians.…”

Section: Discussionmentioning

confidence: 99%

Convolutional Neural Network Techniques for Brain Tumor Classification (from 2015 to 2022): Review, Challenges, and Future Perspectives

et al. 2022

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Convolutional neural networks (CNNs) constitute a widely used deep learning approach that has frequently been applied to the problem of brain tumor diagnosis. Such techniques still face some critical challenges in moving towards clinic application. The main objective of this work is to present a comprehensive review of studies using CNN architectures to classify brain tumors using MR images with the aim of identifying useful strategies for and possible impediments in the development of this technology. Relevant articles were identified using a predefined, systematic procedure. For each article, data were extracted regarding training data, target problems, the network architecture, validation methods, and the reported quantitative performance criteria. The clinical relevance of the studies was then evaluated to identify limitations by considering the merits of convolutional neural networks and the remaining challenges that need to be solved to promote the clinical application and development of CNN algorithms. Finally, possible directions for future research are discussed for researchers in the biomedical and machine learning communities. A total of 83 studies were identified and reviewed. They differed in terms of the precise classification problem targeted and the strategies used to construct and train the chosen CNN. Consequently, the reported performance varied widely, with accuracies of 91.63–100% in differentiating meningiomas, gliomas, and pituitary tumors (26 articles) and of 60.0–99.46% in distinguishing low-grade from high-grade gliomas (13 articles). The review provides a survey of the state of the art in CNN-based deep learning methods for brain tumor classification. Many networks demonstrated good performance, and it is not evident that any specific methodological choice greatly outperforms the alternatives, especially given the inconsistencies in the reporting of validation methods, performance metrics, and training data encountered. Few studies have focused on clinical usability.

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Section: Discussionmentioning

confidence: 99%

Convolutional Neural Network Techniques for Brain Tumor Classification (from 2015 to 2022): Review, Challenges, and Future Perspectives

et al. 2022

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“…Prototypical Part Network (ProtoPNet) [27] is a deep neural network that is interpretable and performs classification by comparing the features extracted from the input image against class discriminative prototypes. ProtoPNet was utilised for Alzheimer's disease classification with DenseNet-121 as a feature extractor and the analysis showed that the ProtoPNet provided reasoning for its prediction that can facilitate its adoption in clinical practice [111]. Anatomical priors and other domain specific information related to the medical image analysis task can be incorporated in the model to make its predictions interpretable.…”

Section: Explainability -For Enhanced Understanding Of Ai In Medical ...mentioning

confidence: 99%

FUTURE-AI: Guiding Principles and Consensus Recommendations for Trustworthy Artificial Intelligence in Medical Imaging

Lekadir,

Osuala,

Gallin

et al. 2021

Preprint

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The recent advancements in artificial intelligence (AI) combined with the extensive amount of data generated by today's clinical systems, has led to the development of imaging AI solutions across the whole value chain of medical imaging, including image reconstruction, medical image segmentation, image-based diagnosis and treatment planning. Notwithstanding the successes and future potential of AI in medical imaging, many stakeholders are concerned of the potential risks and ethical implications of imaging AI solutions, which are perceived as complex, opaque, and difficult to comprehend, utilise, and trust in critical clinical applications. Despite these concerns and risks, there are currently no concrete guidelines and best practices for guiding future AI developments in medical imaging towards increased trust, safety and adoption. To bridge this gap, this paper introduces a careful selection of guiding principles drawn from the accumulated experiences, consensus, and best practices from five large European projects on AI in Health Imaging. These guiding principles are named FUTURE-AI and its building blocks consist of (i) Fairness, (ii) Universality, (iii) Traceability, (iv) Usability, (v) Robustness and (vi) Explainability. In a step-by-step approach, these guidelines are further translated into a framework of concrete recommendations for specifying, developing, evaluating, and deploying technically, clinically and ethically trustworthy AI solutions into clinical practice.

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“…The interpretability of ProtoPNet does not come at a cost of performance when compared to black-box DL models. Mohammadjafari et al [94] utilized ProtoPNet with DenseNet121 architecture for Alzheimer's disease classification. Barnett et al [8] used a modified version of ProtoPNet to utilize fine-grained expert annotations for mass margin classification and malignancy prediction.…”

Section: Case-based Modelsmentioning

confidence: 99%

Transparency of Deep Neural Networks for Medical Image Analysis: A Review of Interpretability Methods

Salahuddin¹,

Woodruff²,

Chatterjee³

et al. 2021

Preprint

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Artificial Intelligence (AI) has emerged as a useful aid in numerous clinical applications for diagnosis and treatment decisions. Deep neural networks have shown same or better performance than clinicians in many tasks owing to the rapid increase in the available data and computational power. In order to conform to the principles of trustworthy AI, it is essential that the AI system be transparent, robust, fair and ensure accountability. Current deep neural solutions are referred to as black-boxes due to a lack of understanding of the specifics concerning the decision making process. Therefore, there is a need to ensure interpretability of deep neural networks before they can be incorporated in the routine clinical workflow. In this narrative review, we utilized systematic keyword searches and domain expertise to identify nine different types of interpretability methods that have been used for understanding deep learning models for medical image analysis applications based on the type of generated explanations and technical similarities. Furthermore, we report the progress made towards evaluating the explanations produced by various interpretability methods. Finally we discuss limitations, provide guidelines for using interpretability methods and future directions concerning the interpretability of deep neural networks for medical imaging analysis.

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Using ProtoPNet for Interpretable Alzheimer’s Disease Classification

Cited by 16 publications

References 18 publications

Convolutional Neural Network Techniques for Brain Tumor Classification (from 2015 to 2022): Review, Challenges, and Future Perspectives

Convolutional Neural Network Techniques for Brain Tumor Classification (from 2015 to 2022): Review, Challenges, and Future Perspectives

FUTURE-AI: Guiding Principles and Consensus Recommendations for Trustworthy Artificial Intelligence in Medical Imaging

Transparency of Deep Neural Networks for Medical Image Analysis: A Review of Interpretability Methods

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