Search Results for author:
Found 8 papers, 4 papers with code
VinDr-Mammo: A large-scale benchmark dataset for computer-aided diagnosis in full-field digital mammography
Mammography, or breast X-ray, is the most widely used imaging modality to detect cancer and other breast diseases.
VinDr-SpineXR: A deep learning framework for spinal lesions detection and classification from radiographs
It demonstrates an area under the receiver operating characteristic curve (AUROC) of 88. 61% (95% CI 87. 19%, 90. 02%) for the image-level classification task and a mean average precision (mAP@0. 5) of 33. 56% for the lesion-level localization task.
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.
Detecting Burnscar from Hyperspectral Imagery via Sparse Representation with Low-Rank Interference
In this paper, we propose a burnscar detection model for hyperspectral imaging (HSI) data.
Multi-task Image Classification via Collaborative, Hierarchical Spike-and-Slab Priors
Promising results have been achieved in image classification problems by exploiting the discriminative power of sparse representations for classification (SRC).
Collaborative Multi-sensor Classification via Sparsity-based Representation
In this paper, we propose a general collaborative sparse representation framework for multi-sensor classification, which takes into account the correlations as well as complementary information between heterogeneous sensors simultaneously while considering joint sparsity within each sensor's observations.
Hierarchical Sparse and Collaborative Low-Rank Representation for Emotion Recognition
In this paper, we design a Collaborative-Hierarchical Sparse and Low-Rank (C-HiSLR) model that is natural for recognizing human emotion in visual data.
Structured Dictionary Learning for Classification
Sparsity driven signal processing has gained tremendous popularity in the last decade.
