VinDr-Mammo: A large-scale benchmark dataset for computer-aided diagnosis in full-field digital mammography

1 code implementation • 20 Mar 2022 • Hieu T. Nguyen, Ha Q. Nguyen, Hieu H. Pham, Khanh Lam, Linh T. Le, Minh Dao, Van Vu

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

1 code implementation • 24 Jun 2021 • Hieu T. Nguyen, Hieu H. Pham, Nghia T. Nguyen, Ha Q. Nguyen, Thang Q. Huynh, Minh Dao, Van Vu

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

3 code implementations • 30 Dec 2020 • Ha Q. Nguyen, Khanh Lam, Linh T. Le, Hieu H. Pham, Dat Q. Tran, Dung B. Nguyen, Dung D. Le, Chi M. Pham, Hang T. T. Tong, Diep H. Dinh, Cuong D. Do, Luu T. Doan, Cuong N. Nguyen, Binh T. Nguyen, Que V. Nguyen, Au D. Hoang, Hien N. Phan, Anh T. Nguyen, Phuong H. Ho, Dat T. Ngo, Nghia T. Nguyen, Nhan T. Nguyen, Minh Dao, Van Vu

Most of the existing chest X-ray datasets include labels from a list of findings without specifying their locations on the radiographs.

Matrices with Gaussian noise: optimal estimates for singular subspace perturbation

no code implementations • 2 Mar 2018 • Sean O'Rourke, Van Vu, Ke Wang

In this paper, we prove a stochastic version of the Davis-Kahan-Wedin $\sin \Theta$ theorem when the perturbation is a Gaussian random matrix.

Matrix Completion