Shifting machine learning for healthcare from development to deployment and from models to data
In the past decade, the application of machine learning (ML) to healthcare has helped drive
the automation of physician tasks as well as enhancements in clinical capabilities and …
the automation of physician tasks as well as enhancements in clinical capabilities and …
[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] Transparency of deep neural networks for medical image analysis: A review of interpretability methods
Artificial Intelligence (AI) has emerged as a useful aid in numerous clinical applications for
diagnosis and treatment decisions. Deep neural networks have shown the same or better …
diagnosis and treatment decisions. Deep neural networks have shown the same or better …
Multimodal machine learning in precision health: A scoping review
Abstract Machine learning is frequently being leveraged to tackle problems in the health
sector including utilization for clinical decision-support. Its use has historically been focused …
sector including utilization for clinical decision-support. Its use has historically been focused …
Contrastive learning of medical visual representations from paired images and text
Learning visual representations of medical images (eg, X-rays) is core to medical image
understanding but its progress has been held back by the scarcity of human annotations …
understanding but its progress has been held back by the scarcity of human annotations …
Interactive and explainable region-guided radiology report generation
The automatic generation of radiology reports has the potential to assist radiologists in the
time-consuming task of report writing. Existing methods generate the full report from image …
time-consuming task of report writing. Existing methods generate the full report from image …
Exploring and distilling posterior and prior knowledge for radiology report generation
Automatically generating radiology reports can improve current clinical practice in diagnostic
radiology. On one hand, it can relieve radiologists from the heavy burden of report writing; …
radiology. On one hand, it can relieve radiologists from the heavy burden of report writing; …
Review of artificial intelligence techniques in imaging data acquisition, segmentation, and diagnosis for COVID-19
The pandemic of coronavirus disease 2019 (COVID-19) is spreading all over the world.
Medical imaging such as X-ray and computed tomography (CT) plays an essential role in …
Medical imaging such as X-ray and computed tomography (CT) plays an essential role in …
Deep convolutional neural network based medical image classification for disease diagnosis
Medical image classification plays an essential role in clinical treatment and teaching tasks.
However, the traditional method has reached its ceiling on performance. Moreover, by using …
However, the traditional method has reached its ceiling on performance. Moreover, by using …
Chexpert: A large chest radiograph dataset with uncertainty labels and expert comparison
J Irvin, P Rajpurkar, M Ko, Y Yu, S Ciurea-Ilcus… - Proceedings of the AAAI …, 2019 - aaai.org
Large, labeled datasets have driven deep learning methods to achieve expert-level
performance on a variety of medical imaging tasks. We present CheXpert, a large dataset …
performance on a variety of medical imaging tasks. We present CheXpert, a large dataset …