Artificial intelligence in surgery: promises and perils
Objective: The aim of this review was to summarize major topics in artificial intelligence (AI),
including their applications and limitations in surgery. This paper reviews the key …
including their applications and limitations in surgery. This paper reviews the key …
Computer‐aided diagnosis in the era of deep learning
Computer‐aided diagnosis (CAD) has been a major field of research for the past few
decades. CAD uses machine learning methods to analyze imaging and/or nonimaging …
decades. CAD uses machine learning methods to analyze imaging and/or nonimaging …
VinDr-CXR: An open dataset of chest X-rays with radiologist's annotations
Most of the existing chest X-ray datasets include labels from a list of findings without
specifying their locations on the radiographs. This limits the development of machine …
specifying their locations on the radiographs. This limits the development of machine …
Transfusion: Understanding transfer learning for medical imaging
Transfer learning from natural image datasets, particularly ImageNet, using standard large
models and corresponding pretrained weights has become a de-facto method for deep …
models and corresponding pretrained weights has become a de-facto method for deep …
Variable generalization performance of a deep learning model to detect pneumonia in chest radiographs: a cross-sectional study
Background There is interest in using convolutional neural networks (CNNs) to analyze
medical imaging to provide computer-aided diagnosis (CAD). Recent work has suggested …
medical imaging to provide computer-aided diagnosis (CAD). Recent work has suggested …
Comparison of deep learning approaches for multi-label chest X-ray classification
The increased availability of labeled X-ray image archives (eg ChestX-ray14 dataset) has
triggered a growing interest in deep learning techniques. To provide better insight into the …
triggered a growing interest in deep learning techniques. To provide better insight into the …
Deep learning in medical image analysis
Deep learning is the state-of-the-art machine learning approach. The success of deep
learning in many pattern recognition applications has brought excitement and high …
learning in many pattern recognition applications has brought excitement and high …
Automated deep learning design for medical image classification by health-care professionals with no coding experience: a feasibility study
Background Deep learning has the potential to transform health care; however, substantial
expertise is required to train such models. We sought to evaluate the utility of automated …
expertise is required to train such models. We sought to evaluate the utility of automated …
Human factors in model interpretability: Industry practices, challenges, and needs
As the use of machine learning (ML) models in product development and data-driven
decision-making processes became pervasive in many domains, people's focus on building …
decision-making processes became pervasive in many domains, people's focus on building …
Automated detection of moderate and large pneumothorax on frontal chest X-rays using deep convolutional neural networks: A retrospective study
Background Pneumothorax can precipitate a life-threatening emergency due to lung
collapse and respiratory or circulatory distress. Pneumothorax is typically detected on chest …
collapse and respiratory or circulatory distress. Pneumothorax is typically detected on chest …