[HTML][HTML] Explainable artificial intelligence (XAI) in deep learning-based medical image analysis
With an increase in deep learning-based methods, the call for explainability of such methods
grows, especially in high-stakes decision making areas such as medical image analysis …
grows, especially in high-stakes decision making areas such as medical image analysis …
[HTML][HTML] A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: a systematic review and meta …
Background Deep learning offers considerable promise for medical diagnostics. We aimed
to evaluate the diagnostic accuracy of deep learning algorithms versus health-care …
to evaluate the diagnostic accuracy of deep learning algorithms versus health-care …
[HTML][HTML] Artificial intelligence distinguishes COVID-19 from community acquired pneumonia on chest CT
Background Coronavirus disease has widely spread all over the world since the beginning
of 2020. It is desirable to develop automatic and accurate detection of COVID-19 using chest …
of 2020. It is desirable to develop automatic and accurate detection of COVID-19 using chest …
Using artificial intelligence to detect COVID-19 and community-acquired pneumonia based on pulmonary CT: evaluation of the diagnostic accuracy
Background Coronavirus disease 2019 (COVID-19) has widely spread all over the world
since the beginning of 2020. It is desirable to develop automatic and accurate detection of …
since the beginning of 2020. It is desirable to develop automatic and accurate detection of …
Fusion of convolution neural network, support vector machine and Sobel filter for accurate detection of COVID-19 patients using X-ray images
Abstract The coronavirus (COVID-19) is currently the most common contagious disease
which is prevalent all over the world. The main challenge of this disease is the primary …
which is prevalent all over the world. The main challenge of this disease is the primary …
Machine learning in action: stroke diagnosis and outcome prediction
The application of machine learning has rapidly evolved in medicine over the past decade.
In stroke, commercially available machine learning algorithms have already been …
In stroke, commercially available machine learning algorithms have already been …
[HTML][HTML] A deep learning algorithm for automatic detection and classification of acute intracranial hemorrhages in head CT scans
Acute Intracranial hemorrhage (ICH) is a life-threatening disease that requires emergency
medical attention, which is routinely diagnosed using non-contrast head CT imaging. The …
medical attention, which is routinely diagnosed using non-contrast head CT imaging. The …
Advances in deep learning-based medical image analysis
Importance. With the booming growth of artificial intelligence (AI), especially the recent
advancements of deep learning, utilizing advanced deep learning-based methods for …
advancements of deep learning, utilizing advanced deep learning-based methods for …
Intracranial hemorrhage segmentation using a deep convolutional model
MD Hssayeni, MS Croock, AD Salman, HF Al-Khafaji… - Data, 2020 - mdpi.com
Traumatic brain injuries may cause intracranial hemorrhages (ICH). ICH could lead to
disability or death if it is not accurately diagnosed and treated in a time-sensitive procedure …
disability or death if it is not accurately diagnosed and treated in a time-sensitive procedure …
Accurate and efficient intracranial hemorrhage detection and subtype classification in 3D CT scans with convolutional and long short-term memory neural networks
In this paper, we present our system for the RSNA Intracranial Hemorrhage Detection
challenge, which is based on the RSNA 2019 Brain CT Hemorrhage dataset. The proposed …
challenge, which is based on the RSNA 2019 Brain CT Hemorrhage dataset. The proposed …