280 citations · 386 across the 8 of their papers we have counts for
5 papers · 1 filter
Iterative Reorganization with Weak Spatial Constraints: Solving Arbitrary Jigsaw Puzzles for Unsupervised Representation Learning
Chen Wei, Lingxi Xie, Xutong Ren +5
Learning visual features from unlabeled image data is an important yet challenging task, which is often achieved by training a model on some annotation-free information. We conside…
3D Semi-Supervised Learning with Uncertainty-Aware Multi-View Co-Training
Yingda Xia, Fengze Liu, Dong Yang +6
While making a tremendous impact in various fields, deep neural networks usually require large amounts of labeled data for training which are expensive to collect in many applicati…
Multi-Scale Coarse-to-Fine Segmentation for Screening Pancreatic Ductal Adenocarcinoma
Zhuotun Zhu, Yingda Xia, Lingxi Xie +2
We propose an intuitive approach of detecting pancreatic ductal adenocarcinoma (PDAC), the most common type of pancreatic cancer, by checking abdominal CT scans. Our idea is named…
Joint Shape Representation and Classification for Detecting PDAC
Fengze Liu, Lingxi Xie, Yingda Xia +2
We aim to detect pancreatic ductal adenocarcinoma (PDAC) in abdominal CT scans, which sheds light on early diagnosis of pancreatic cancer. This is a 3D volume classification task w…
Bridging the Gap Between 2D and 3D Organ Segmentation with Volumetric Fusion Net
Yingda Xia, Lingxi Xie, Fengze Liu +3
There has been a debate on whether to use 2D or 3D deep neural networks for volumetric organ segmentation. Both 2D and 3D models have their advantages and disadvantages. In this pa…