Application of explainable artificial intelligence for healthcare: A systematic review of the last decade (2011–2022)
Background and objectives Artificial intelligence (AI) has branched out to various
applications in healthcare, such as health services management, predictive medicine …
applications in healthcare, such as health services management, predictive medicine …
Survey of explainable AI techniques in healthcare
Artificial intelligence (AI) with deep learning models has been widely applied in numerous
domains, including medical imaging and healthcare tasks. In the medical field, any judgment …
domains, including medical imaging and healthcare tasks. In the medical field, any judgment …
Criteria for the translation of radiomics into clinically useful tests
EP Huang, JPB O'Connor, LM McShane… - Nature reviews Clinical …, 2023 - nature.com
Computer-extracted tumour characteristics have been incorporated into medical imaging
computer-aided diagnosis (CAD) algorithms for decades. With the advent of radiomics, an …
computer-aided diagnosis (CAD) algorithms for decades. With the advent of radiomics, an …
On the analyses of medical images using traditional machine learning techniques and convolutional neural networks
Convolutional neural network (CNN) has shown dissuasive accomplishment on different
areas especially Object Detection, Segmentation, Reconstruction (2D and 3D), Information …
areas especially Object Detection, Segmentation, Reconstruction (2D and 3D), Information …
Explainable medical imaging AI needs human-centered design: guidelines and evidence from a systematic review
Abstract Transparency in Machine Learning (ML), often also referred to as interpretability or
explainability, attempts to reveal the working mechanisms of complex models. From a …
explainability, attempts to reveal the working mechanisms of complex models. From a …
Pneumonia detection on chest X-ray images using ensemble of deep convolutional neural networks
Pneumonia is a life-threatening lung infection resulting from several different viral infections.
Identifying and treating pneumonia on chest X-ray images can be difficult due to its similarity …
Identifying and treating pneumonia on chest X-ray images can be difficult due to its similarity …
Explainable deep learning methods in medical image classification: A survey
The remarkable success of deep learning has prompted interest in its application to medical
imaging diagnosis. Even though state-of-the-art deep learning models have achieved …
imaging diagnosis. Even though state-of-the-art deep learning models have achieved …
[HTML][HTML] Lung nodule diagnosis and cancer histology classification from computed tomography data by convolutional neural networks: A survey
Lung cancer is among the deadliest cancers. Besides lung nodule classification and
diagnosis, developing non-invasive systems to classify lung cancer histological …
diagnosis, developing non-invasive systems to classify lung cancer histological …
AAPM task group report 273: recommendations on best practices for AI and machine learning for computer‐aided diagnosis in medical imaging
Rapid advances in artificial intelligence (AI) and machine learning, and specifically in deep
learning (DL) techniques, have enabled broad application of these methods in health care …
learning (DL) techniques, have enabled broad application of these methods in health care …
A survey on neural-symbolic learning systems
D Yu, B Yang, D Liu, H Wang, S Pan - Neural Networks, 2023 - Elsevier
In recent years, neural systems have demonstrated highly effective learning ability and
superior perception intelligence. However, they have been found to lack effective reasoning …
superior perception intelligence. However, they have been found to lack effective reasoning …