162 citations · 247 across the 11 of their papers we have counts for
11 papers
DuAT: Dual-Aggregation Transformer Network for Medical Image Segmentation
Feilong Tang, Qiming Huang, Jinfeng Wang +3
Transformer-based models have been widely demonstrated to be successful in computer vision tasks by modelling long-range dependencies and capturing global representations. However,…
Geometry-aware Single-image Full-body Human Relighting
Chaonan Ji, Tao Yu, Kaiwen Guo +2
Single-image human relighting aims to relight a target human under new lighting conditions by decomposing the input image into albedo, shape and lighting. Although plausible religh…
Mitosis domain generalization in histopathology images -- The MIDOG challenge
Marc Aubreville, Nikolas Stathonikos, Christof A. Bertram +32
The density of mitotic figures within tumor tissue is known to be highly correlated with tumor proliferation and thus is an important marker in tumor grading. Recognition of mitoti…
Domain Adaptive Cascade R-CNN for MItosis DOmain Generalization (MIDOG) Challenge
Xi Long, Ying Cheng, Xiao Mu +2
We present a summary of the domain adaptive cascade R-CNN method for mitosis detection of digital histopathology images. By comprehensive data augmentation and adapting existing po…
Class-Aware Domain Adaptation for Improving Adversarial Robustness
Xianxu Hou, Jingxin Liu, Bolei Xu +3
Recent works have demonstrated convolutional neural networks are vulnerable to adversarial examples, i.e., inputs to machine learning models that an attacker has intentionally desi…
Dual Adaptive Pyramid Network for Cross-Stain Histopathology Image Segmentation
Xianxu Hou, Jingxin Liu, Bolei Xu +6
Supervised semantic segmentation normally assumes the test data being in a similar data domain as the training data. However, in practice, the domain mismatch between the training…
Pathological Myopic Image Analysis with Transfer Learning
Ruitao Xie, Libo Liu, Jingxin Liu +1
We present a summary of transfer learning based methods for several challenging myopic fundus image analysis tasks including classification of pathological and non-pathological myo…
Learning Deep Image Priors for Blind Image Denoising
Xianxu Hou, Hongming Luo, Jingxin Liu +5
Image denoising is the process of removing noise from noisy images, which is an image domain transferring task, i.e., from a single or several noise level domains to a photo-realis…
Look, Investigate, and Classify: A Deep Hybrid Attention Method for Breast Cancer Classification
Bolei Xu, Jingxin Liu, Xianxu Hou +6
One issue with computer based histopathology image analysis is that the size of the raw image is usually very large. Taking the raw image as input to the deep learning model would…
Outline Objects using Deep Reinforcement Learning
Zhenxin Wang, Sayan Sarcar, Jingxin Liu +2
Image segmentation needs both local boundary position information and global object context information. The performance of the recent state-of-the-art method, fully convolutional…
An End-to-End Deep Learning Histochemical Scoring System for Breast Cancer Tissue Microarray
Jingxin Liu, Bolei Xu, Chi Zheng +7
One of the methods for stratifying different molecular classes of breast cancer is the Nottingham Prognostic Index Plus (NPI+) which uses breast cancer relevant biomarkers to stain…