Explainable CNN-attention Networks (C-Attention Network) for Automated Detection of Alzheimer’s Disease

In recent years, advances in artificial intelligence (AI) technology have led to the rapid clinical implementation of devices with AI technology in the medical field. More than 60 AI-equipped medical devices have already been approved by the Food and Drug Administration (FDA) in the United States, and the active introduction of AI technology is considered to be an inevitable trend in the future of medicine. In the field of oncology, clinical applications of medical devices using AI technology are already underway, mainly in radiology, and AI technology is expected to be positioned as an important core technology. In particular, “precision medicine,” a medical treatment that selects the most appropriate treatment for each patient based on a vast amount of medical data such as genome information, has become a worldwide trend; AI technology is expected to be utilized in the process of extracting truly useful information from a large amount of medical data and applying it to diagnosis and treatment. In this review, we would like to introduce the history of AI technology and the current state of medical AI, especially in the oncology field, as well as discuss the possibilities and challenges of AI technology in the medical field.

show abstract

“…Deep explanation: deep learning state analysis to generate attention heat maps and natural language explanations [163][164][165].…”

Section: Black Box Problemmentioning

confidence: 99%

Application of Artificial Intelligence Technology in Oncology: Towards the Establishment of Precision Medicine

Hamamoto

Suvarna

Yamada³

et al. 2020

Cancers

136

View full text Add to dashboard Cite

show abstract

“…Figure 1 depicts our C-Attention network which uses latent features to detect suicide attempts. This network is similar to our prior C-Attention Embedding model [36] with the following differences:…”

Section: C-attention Networkmentioning

confidence: 99%

“…• In this work we consider each post as a small document, and use Doc2Vec to generate a 100-dimension embedding representation for each post; whereas the work in [36] generated a sentence embedding for each sentence in a speech. • We removed the positional encoding layer since there is no positional dependency among posts.…”

Section: C-attention Networkmentioning

confidence: 99%

Learning Models for Suicide Prediction from Social Media Posts

Wang

Luo

Shivtare

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

We propose a deep learning architecture and test three other machine learning models to automatically detect individuals that will attempt suicide within (1) 30 days and (2) six months, using their social media post data provided in [1] via the CLPsych 2021 shared task. Additionally, we create and extract three sets of handcrafted features for suicide risk detection based on the three-stage theory of suicide and prior work on emotions and the use of pronouns among persons exhibiting suicidal ideations. Extensive experimentations show that some of the traditional machine learning methods outperform the baseline with an F1 score of 0.741 and F2 score of 0.833 on subtask 1 (prediction of a suicide attempt 30 days prior). However, the proposed deep learning method outperforms the baseline with F1 score of 0.737 and F2 score of 0.843 on subtask 2 (prediction of suicide 6 months prior). * This work is accepted to CLPsych 2021, to be held in conjunction with NAACL 2021 † Equal contribution to 2nd author 3 https://www.who.int/ 4 https://www.cdc.gov/

show abstract

“…ML and DL algorithms for ARD classification and detection have gained much attention in the past decade. With the growth in computational power and emergence of more sophisticated and supervised algorithms like convolutional neural network (CNN) development of artificial intelligence (AI) application in healthcare has increased rapidly [13,14]. All artificial intelligence models will use some training data such as pictures from neuroimaging techniques and other electronic healthcare data to extract full features or direct samples to classify, detect, and recognize ARD.…”

Section: Introductionmentioning

confidence: 99%

Single and Combined Neuroimaging Techniques for Alzheimer’s Disease Detection

Amini

Pedram

Moradi

et al. 2021

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

Alzheimer’s disease (AD) consists of the gradual process of decreasing volume and quality of neuron connection in the brain, which consists of gradual synaptic integrity and loss of cognitive functions. In recent years, there has been significant attention in AD classification and early detection with machine learning algorithms. There are different neuroimaging techniques for capturing data and using it for the classification task. Input data as images will help machine learning models to detect different biomarkers for AD classification. This marker has a more critical role for AD detection than other diseases because beta-amyloid can extract complex structures with some metal ions. Most researchers have focused on using 3D and 4D convolutional neural networks for AD classification due to reasonable amounts of data. Also, combination neuroimaging techniques like functional magnetic resonance imaging and positron emission tomography for AD detection have recently gathered much attention. However, gathering a combination of data can be expensive, complex, and tedious. For time consumption reasons, most patients prefer to throw one of the neuroimaging techniques. So, in this review article, we have surveyed different research studies with various neuroimaging techniques and ML methods to see the effect of using combined data as input. The result has shown that the use of the combination method would increase the accuracy of AD detection. Also, according to the sensitivity metrics from different machine learning methods, MRI and fMRI showed promising results.

show abstract

Explainable CNN-attention Networks (C-Attention Network) for Automated Detection of Alzheimer’s Disease

Cited by 17 publications

References 32 publications

Application of Artificial Intelligence Technology in Oncology: Towards the Establishment of Precision Medicine

Application of Artificial Intelligence Technology in Oncology: Towards the Establishment of Precision Medicine

Learning Models for Suicide Prediction from Social Media Posts

Single and Combined Neuroimaging Techniques for Alzheimer’s Disease Detection

Contact Info

Product

Resources

About