Not-so-supervised: a survey of semi-supervised, multi-instance, and transfer learning in medical image analysis
Abstract Machine learning (ML) algorithms have made a tremendous impact in the field of
medical imaging. While medical imaging datasets have been growing in size, a challenge …
medical imaging. While medical imaging datasets have been growing in size, a challenge …
Towards label-efficient automatic diagnosis and analysis: a comprehensive survey of advanced deep learning-based weakly-supervised, semi-supervised and self …
Histopathological images contain abundant phenotypic information and pathological
patterns, which are the gold standards for disease diagnosis and essential for the prediction …
patterns, which are the gold standards for disease diagnosis and essential for the prediction …
Deep feature learning for medical image analysis with convolutional autoencoder neural network
At present, computed tomography (CT) is widely used to assist disease diagnosis.
Especially, computer aided diagnosis (CAD) based on artificial intelligence (AI) recently …
Especially, computer aided diagnosis (CAD) based on artificial intelligence (AI) recently …
A semi-supervised learning approach for COVID-19 detection from chest CT scans
COVID-19 has spread rapidly all over the world and has infected more than 200 countries
and regions. Early screening of suspected infected patients is essential for preventing and …
and regions. Early screening of suspected infected patients is essential for preventing and …
Deep semi-supervised knowledge distillation for overlapping cervical cell instance segmentation
Deep learning methods show promising results for overlapping cervical cell instance
segmentation. However, in order to train a model with good generalization ability …
segmentation. However, in order to train a model with good generalization ability …
Deep-learning-based automated neuron reconstruction from 3D microscopy images using synthetic training images
W Chen, M Liu, H Du, M Radojević… - … on Medical Imaging, 2021 - ieeexplore.ieee.org
Digital reconstruction of neuronal structures from 3D microscopy images is critical for the
quantitative investigation of brain circuits and functions. It is a challenging task that would …
quantitative investigation of brain circuits and functions. It is a challenging task that would …
Computer-aided breast cancer diagnosis: Comparative analysis of breast imaging modalities and mammogram repositories
The accurate assessment or diagnosis of breast cancer depends on image acquisition and
image analysis and interpretation. The expert radiologist makes image interpretation, and …
image analysis and interpretation. The expert radiologist makes image interpretation, and …
Semixup: In- and Out-of-Manifold Regularization for Deep Semi-Supervised Knee Osteoarthritis Severity Grading From Plain Radiographs
HH Nguyen, S Saarakkala… - IEEE Transactions on …, 2020 - ieeexplore.ieee.org
Knee osteoarthritis (OA) is one of the highest disability factors in the world. This
musculoskeletal disorder is assessed from clinical symptoms, and typically confirmed via …
musculoskeletal disorder is assessed from clinical symptoms, and typically confirmed via …
Real-time halo correction in phase contrast imaging
As a label-free, nondestructive method, phase contrast is by far the most popular microscopy
technique for routine inspection of cell cultures. However, features of interest such as …
technique for routine inspection of cell cultures. However, features of interest such as …
Automated red blood cells extraction from holographic images using fully convolutional neural networks
In this paper, we present two models for automatically extracting red blood cells (RBCs) from
RBCs holographic images based on a deep learning fully convolutional neural network …
RBCs holographic images based on a deep learning fully convolutional neural network …