Machine learning in medical applications: A review of state-of-the-art methods
Applications of machine learning (ML) methods have been used extensively to solve various
complex challenges in recent years in various application areas, such as medical, financial …
complex challenges in recent years in various application areas, such as medical, financial …
Machine learning in scanning transmission electron microscopy
Scanning transmission electron microscopy (STEM) has emerged as a uniquely powerful
tool for structural and functional imaging of materials on the atomic level. Driven by …
tool for structural and functional imaging of materials on the atomic level. Driven by …
[HTML][HTML] Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation
Automatic segmentation methods are an important advancement in medical image analysis.
Machine learning techniques, and deep neural networks in particular, are the state-of-the-art …
Machine learning techniques, and deep neural networks in particular, are the state-of-the-art …
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 …
Uncertainty quantification in skin cancer classification using three-way decision-based Bayesian deep learning
Accurate automated medical image recognition, including classification and segmentation,
is one of the most challenging tasks in medical image analysis. Recently, deep learning …
is one of the most challenging tasks in medical image analysis. Recently, deep learning …
[HTML][HTML] Smart healthcare: making medical care more intelligent
S Tian, W Yang, JM Le Grange, P Wang, W Huang… - Global Health …, 2019 - Elsevier
With the development of information technology, the concept of smart healthcare has
gradually come to the fore. Smart healthcare uses a new generation of information …
gradually come to the fore. Smart healthcare uses a new generation of information …
Current applications and future impact of machine learning in radiology
Recent advances and future perspectives of machine learning techniques offer promising
applications in medical imaging. Machine learning has the potential to improve different …
applications in medical imaging. Machine learning has the potential to improve different …
eD octor: machine learning and the future of medicine
GS Handelman, HK Kok, RV Chandra… - Journal of internal …, 2018 - Wiley Online Library
Abstract Machine learning (ML) is a burgeoning field of medicine with huge resources being
applied to fuse computer science and statistics to medical problems. Proponents of ML extol …
applied to fuse computer science and statistics to medical problems. Proponents of ML extol …
Deep learning at chest radiography: automated classification of pulmonary tuberculosis by using convolutional neural networks
P Lakhani, B Sundaram - Radiology, 2017 - pubs.rsna.org
Purpose To evaluate the efficacy of deep convolutional neural networks (DCNNs) for
detecting tuberculosis (TB) on chest radiographs. Materials and Methods Four deidentified …
detecting tuberculosis (TB) on chest radiographs. Materials and Methods Four deidentified …
Convolutional neural network improvement for breast cancer classification
Traditionally, physicians need to manually delineate the suspected breast cancer area.
Numerous studies have mentioned that manual segmentation takes time, and depends on …
Numerous studies have mentioned that manual segmentation takes time, and depends on …