79 citations · 156 across the 15 of their papers we have counts for
8 papers · 1 filter
Label-Efficient Object Detection via Region Proposal Network Pre-Training
Nanqing Dong, Linus Ericsson, Yongxin Yang +2
Self-supervised pre-training, based on the pretext task of instance discrimination, has fueled the recent advance in label-efficient object detection. However, existing studies foc…
Learning Underrepresented Classes from Decentralized Partially Labeled Medical Images
Nanqing Dong, Michael Kampffmeyer, Irina Voiculescu
Using decentralized data for federated training is one promising emerging research direction for alleviating data scarcity in the medical domain. However, in contrast to large-scal…
Residual Contrastive Learning for Image Reconstruction: Learning Transferable Representations from Noisy Images
Nanqing Dong, Matteo Maggioni, Yongxin Yang +3
This paper is concerned with contrastive learning (CL) for low-level image restoration and enhancement tasks. We propose a new label-efficient learning paradigm based on residuals,…
Towards Robust Partially Supervised Multi-Structure Medical Image Segmentation on Small-Scale Data
Nanqing Dong, Michael Kampffmeyer, Xiaodan Liang +3
The data-driven nature of deep learning (DL) models for semantic segmentation requires a large number of pixel-level annotations. However, large-scale and fully labeled medical dat…
Reinforced Auto-Zoom Net: Towards Accurate and Fast Breast Cancer Segmentation in Whole-slide Images
Nanqing Dong, Michael Kampffmeyer, Xiaodan Liang +3
Convolutional neural networks have led to significant breakthroughs in the domain of medical image analysis. However, the task of breast cancer segmentation in whole-slide images (…
Unsupervised Domain Adaptation for Automatic Estimation of Cardiothoracic Ratio
Nanqing Dong, Michael Kampffmeyer, Xiaodan Liang +3
The cardiothoracic ratio (CTR), a clinical metric of heart size in chest X-rays (CXRs), is a key indicator of cardiomegaly. Manual measurement of CTR is time-consuming and can be a…
ConnNet: A Long-Range Relation-Aware Pixel-Connectivity Network for Salient Segmentation
Michael Kampffmeyer, Nanqing Dong, Xiaodan Liang +2
Salient segmentation aims to segment out attention-grabbing regions, a critical yet challenging task and the foundation of many high-level computer vision applications. It requires…
SCAN: Structure Correcting Adversarial Network for Organ Segmentation in Chest X-rays
Wei Dai, Joseph Doyle, Xiaodan Liang +4
Chest X-ray (CXR) is one of the most commonly prescribed medical imaging procedures, often with over 2-10x more scans than other imaging modalities such as MRI, CT scan, and PET sc…