Triplet attention and dual-pool contrastive learning for clinic-driven multi-label medical image classification
Multi-label classification (MLC) can attach multiple labels on single image, and has
achieved promising results on medical images. But existing MLC methods still face …
achieved promising results on medical images. But existing MLC methods still face …
Supervised Feature Selection via Multi-Center and Local Structure Learning
Feature selection has achieved unprecedented success in obtaining sparse discriminative
features. However, the existing methods almost use the-norm constraint on transformation …
features. However, the existing methods almost use the-norm constraint on transformation …
Detection of post-COVID-19-related pulmonary diseases in X-ray images using Vision Transformer-based neural network
Objective: Computer methods related to the diagnosis of COVID-19 disease have
progressed significantly in recent years. Chest X-ray analysis supported by artificial …
progressed significantly in recent years. Chest X-ray analysis supported by artificial …
PCAN: Pixel-wise classification and attention network for thoracic disease classification and weakly supervised localization
X Zhu, S Pang, X Zhang, J Huang, L Zhao… - … Medical Imaging and …, 2022 - Elsevier
Automatic chest X-ray (CXR) disease classification has drawn increasing public attention as
CXR is widely used in thoracic disease diagnosis. Existing classification networks typically …
CXR is widely used in thoracic disease diagnosis. Existing classification networks typically …
Entangled View-Epipolar Information Aggregation for Generalizable Neural Radiance Fields
Generalizable NeRF can directly synthesize novel views across new scenes eliminating the
need for scene-specific retraining in vanilla NeRF. A critical enabling factor in these …
need for scene-specific retraining in vanilla NeRF. A critical enabling factor in these …
Hydravit: Adaptive multi-branch transformer for multi-label disease classification from chest X-ray images
Chest X-ray is an essential diagnostic tool in the identification of chest diseases given its
high sensitivity to pathological abnormalities in the lungs. However, image-driven diagnosis …
high sensitivity to pathological abnormalities in the lungs. However, image-driven diagnosis …
Cross-modal contrastive attention model for medical report generation
Medical report automatic generation has gained increasing interest recently as a way to help
radiologists write reports more efficiently. However, this image-to-text task is rather …
radiologists write reports more efficiently. However, this image-to-text task is rather …
Joint representation and classifier learning for long-tailed image classification
Long-tailed classification with fine-grained appearance, eg, in chest X-ray images, is very
challenging due to the very similar appearance and imbalanced distribution between normal …
challenging due to the very similar appearance and imbalanced distribution between normal …
BFENet: A two-stream interaction CNN method for multi-label ophthalmic diseases classification with bilateral fundus images
X Ou, L Gao, X Quan, H Zhang, J Yang, W Li - Computer Methods and …, 2022 - Elsevier
Background and objective Early fundus screening and timely treatment of ophthalmology
diseases can effectively prevent blindness. Previous studies just focus on fundus images of …
diseases can effectively prevent blindness. Previous studies just focus on fundus images of …
Multi-level residual feature fusion network for thoracic disease classification in chest X-ray images
Automated identification of thoracic diseases from chest X-ray images (CXR) is a significant
area in computer-aided diagnosis. However, most existing methods have limited ability to …
area in computer-aided diagnosis. However, most existing methods have limited ability to …