Search Results for author:
Found 8 papers, 4 papers with code
Multi-Scale Feature Fusion using Parallel-Attention Block for COVID-19 Chest X-ray Diagnosis
Under the global COVID-19 crisis, accurate diagnosis of COVID-19 from Chest X-ray (CXR) images is critical.
Multi-Feature Vision Transformer via Self-Supervised Representation Learning for Improvement of COVID-19 Diagnosis
To improve the diagnostic performance of CXR imaging a growing number of studies have investigated whether supervised deep learning methods can provide additional support.
Multi-Feature Semi-Supervised Learning for COVID-19 Diagnosis from Chest X-ray Images
The requirement for a large amount of labeled data is one of the major problems of deep learning methods when deployed in the medical domain.
Chest X-ray Image Phase Features for Improved Diagnosis of COVID-19 Using Convolutional Neural Network
The enhanced images, together with the original CXR data, are used as an input to our proposed CNN architecture.
EIGEN: Ecologically-Inspired GENetic Approach for Neural Network Structure Searching from Scratch
In this paper, we propose an Ecologically-Inspired GENetic (EIGEN) approach that uses the concept of succession, extinction, mimicry, and gene duplication to search neural network structure from scratch with poorly initialized simple network and few constraints forced during the evolution, as we assume no prior knowledge about the task domain.
Factorized Adversarial Networks for Unsupervised Domain Adaptation
In this paper, we propose Factorized Adversarial Networks (FAN) to solve unsupervised domain adaptation problems for image classification tasks.
Adversarial Domain Adaptation for Classification of Prostate Histopathology Whole-Slide Images
Automatic and accurate Gleason grading of histopathology tissue slides is crucial for prostate cancer diagnosis, treatment, and prognosis.
Personalized Image Aesthetics
To accommodate our study, we first collect two distinct datasets, a large image dataset from Flickr and annotated by Amazon Mechanical Turk, and a small dataset of real personal albums rated by owners.
