Computer-Aided Diagnosis of Alzheimer’s Disease through Weak Supervision Deep Learning Framework with Attention Mechanism

Liang, Shuang; Gu, Yu

doi:10.3390/s21010220

Cited by 49 publications

(19 citation statements)

References 38 publications

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“…When objects appear, more attention will be paid to key information and useless information will be suppressed at the same time. In recent years, the relationship between computer vision tasks and attention mechanism has become increasingly close, such as residual network with attention mechanism for Alzheimer's disease (AD) recognition and classification [ 22 ]. A network with attention mechanism is used to detect bridges in synthetic aperture radar (SAR) images [ 23 ].…”

Section: Related Workmentioning

confidence: 99%

Research on Real-Time Face Key Point Detection Algorithm Based on Attention Mechanism

Gao

Yang

2022

Computational Intelligence and Neuroscience

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The existing face detection methods were affected by the network model structure used. Most of the face recognition methods had low recognition rate of face key point features due to many parameters and large amount of calculation. In order to improve the recognition accuracy and detection speed of face key points, a real-time face key point detection algorithm based on attention mechanism was proposed in this paper. Due to the multiscale characteristics of face key point features, the deep convolution network model was adopted, the attention module was added to the VGG network structure, the feature enhancement module and feature fusion module were combined to improve the shallow feature representation ability of VGG, and the cascade attention mechanism was used to improve the deep feature representation ability. Experiments showed that the proposed algorithm not only can effectively realize face key point recognition but also has better recognition accuracy and detection speed than other similar methods. This method can provide some theoretical basis and technical support for face detection in complex environment.

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Section: Related Workmentioning

confidence: 99%

Research on Real-Time Face Key Point Detection Algorithm Based on Attention Mechanism

Gao

Yang

2022

Computational Intelligence and Neuroscience

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“…This data set consists of MRI images of brains, and the aim is to discriminate the presence or absence of Alzheimer’s disease and, in the positive case, to assess its stage. The images were also segmented by the data set creators, and, as of today, the data set has been downloaded 9422 times, so it is widely known and used in the literature in recent papers, as in, e.g., [ 58 , 59 , 60 ].…”

Section: The Data Setmentioning

confidence: 99%

A Two-Step Approach for Classification in Alzheimer’s Disease

Falco

Pietro

Sannino

2022

Sensors

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The classification of images is of high importance in medicine. In this sense, Deep learning methodologies show excellent performance with regard to accuracy. The drawback of these methodologies is the fact that they are black boxes, so no explanation is given to users on the reasons underlying their choices. In the medical domain, this lack of transparency and information, typical of black box models, brings practitioners to raise concerns, and the result is a resistance to the use of deep learning tools. In order to overcome this problem, a different Machine Learning approach to image classification is used here that is based on interpretability concepts thanks to the use of an evolutionary algorithm. It relies on the application of two steps in succession. The first receives a set of images in the inut and performs image filtering on them so that a numerical data set is generated. The second is a classifier, the kernel of which is an evolutionary algorithm. This latter, at the same time, classifies and automatically extracts explicit knowledge as a set of IF–THEN rules. This method is investigated with respect to a data set of MRI brain imagery referring to Alzheimer’s disease. Namely, a two-class data set (non-demented and moderate demented) and a three-class data set (non-demented, mild demented, and moderate demented) are extracted. The methodology shows good results in terms of accuracy (100% for the best run over the two-class problem and 91.49% for the best run over the three-class one), F_score (1.0000 and 0.9149, respectively), and Matthews Correlation Coefficient (1.0000 and 0.8763, respectively). To ascertain the quality of these results, they are contrasted against those from a wide set of well-known classifiers. The outcome of this comparison is that, in both problems, the methodology achieves the best results in terms of accuracy and F_score, whereas, for the Matthews Correlation Coefficient, it has the best result over the two-class problem and the second over the three-class one.

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“…Gong et al Gong et al [2022] addressed the reconstruction of linear parametric images from dynamic positron emission tomography (PET) and implemented a 3D U-Net Çiçek et al [2016] model with an attention layer that focuses on prior anatomical information during training to improve the quality of the reconstruction of dynamic PET images of the human brain. Liang and Gu Liang and Gu [2021] also explored the reconstruction of brain MRI data as a regularisation strategy to aid the diagnosis of Alzheimer's disease.…”

Section: Medical Image Reconstructionmentioning

confidence: 99%

“…Several authors have addressed the task of diabetic retinopathy recognition in a multi-class setting (i.e., no diabetic retinopathy, mild non-proliferative diabetic retinopathy, moderate diabetic retinopathy, severe non-proliferative diabetic retinopathy, and proliferative diabetic retinopathy) with a Vision Transformer architectureWu et al Datta et al Datta et al [2021] performed a comparative study of the effect of soft-attention combined with different backbone architectures in skin cancer classification and reported several advantages against conventional methodologies. Barhoumi and GhulamBarhoumi and Ghulam [2021] proposed a model that aggregates several feature maps extracted using multiple Xception CNNsChollet [2017] and uses these features to train a Vision Transformer for the intracranial hemorrhage classification problem, using CT images Liang and Gu Liang and Gu [2021]. added an attention mechanism to a backbone network based on the ResNetHe et al [2016] to aid the diagnosis of Alzheimer's disease in brain MRI data.Using well-known data sets related to different use cases and working on top ofHu et al [2018a], Roy et alRoy et al [2018] verified that the addition of a spatial-channel squeeze and excitation block works as an attention mechanism in FCNs and improves the quality of the segmentation maps.…”

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confidence: 99%

A survey on attention mechanisms for medical applications: are we moving towards better algorithms?

Gonçalves¹,

Rio-Torto²,

Teixeira³

et al. 2022

Preprint

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The increasing popularity of attention mechanisms in deep learning algorithms for computer vision and natural language processing made these models attractive to other research domains. In healthcare, there is a strong need for tools that may improve the routines of the clinicians and the patients. Naturally, the use of attention-based algorithms for medical applications occurred smoothly. However, being healthcare a domain that depends on high-stake decisions, the scientific community must ponder if these high-performing algorithms fit the needs of medical applications. With this motto, this paper extensively reviews the use of attention mechanisms in machine learning (including Transformers) for several medical applications. This work distinguishes itself from its predecessors by proposing a critical analysis of the claims and potentialities of attention mechanisms presented in the literature through an experimental case study on medical image classification with three different use cases. These experiments focus on the integrating process of attention mechanisms into established deep learning architectures, the analysis of their predictive power, and a visual assessment of their saliency maps generated by post-hoc explanation methods. This paper concludes with a critical analysis of the claims and potentialities presented in the literature about attention mechanisms and proposes future research lines in medical applications that may benefit from these frameworks.

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Computer-Aided Diagnosis of Alzheimer’s Disease through Weak Supervision Deep Learning Framework with Attention Mechanism

Cited by 49 publications

References 38 publications

Research on Real-Time Face Key Point Detection Algorithm Based on Attention Mechanism

Research on Real-Time Face Key Point Detection Algorithm Based on Attention Mechanism

A Two-Step Approach for Classification in Alzheimer’s Disease

A survey on attention mechanisms for medical applications: are we moving towards better algorithms?

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