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- Ministry of Industry and Information TechnologyCN16 papers
- Tsinghua UniversityCN16 papers
- Chinese Academy of SciencesCN15 papers
- Xi'an Jiaotong UniversityCN15 papers
- Xidian UniversityCN12 papers
- Australian National UniversityAU11 papers
- Moscow Institute of Physics and TechnologyRU11 papers
- University of Chinese Academy of SciencesCN10 papers
- Nankai UniversityCN9 papers
- Centre National de la Recherche ScientifiqueFR7 papers
- Fudan UniversityCN7 papers
- Instituto de Ciencia de Materiales de MadridES7 papers
12 papers · 1 filter
Low-Light Hyperspectral Image Enhancement
Xuelong Li, Guanlin Li, Bin Zhao
Due to inadequate energy captured by the hyperspectral camera sensor in poor illumination conditions, low-light hyperspectral images (HSIs) usually suffer from low visibility, spec…
Cross-Modality Deep Feature Learning for Brain Tumor Segmentation
Dingwen Zhang, Guohai Huang, Qiang Zhang +3
Recent advances in machine learning and prevalence of digital medical images have opened up an opportunity to address the challenging brain tumor segmentation (BTS) task by using d…
Task-Related Self-Supervised Learning for Remote Sensing Image Change Detection
Zhinan Cai, Zhiyu Jiang, Yuan Yuan
Change detection for remote sensing images is widely applied for urban change detection, disaster assessment and other fields. However, most of the existing CNN-based change detect…
Learning from Ambiguous Labels for Lung Nodule Malignancy Prediction
Zehui Liao, Yutong Xie, Shishuai Hu +1
Lung nodule malignancy prediction is an essential step in the early diagnosis of lung cancer. Besides the difficulties commonly discussed, the challenges of this task also come fro…
A Two-Stage Attentive Network for Single Image Super-Resolution
Jiqing Zhang, Chengjiang Long, Yuxin Wang +4
Recently, deep convolutional neural networks (CNNs) have been widely explored in single image super-resolution (SISR) and contribute remarkable progress. However, most of the exist…
A Structure-Aware Relation Network for Thoracic Diseases Detection and Segmentation
Jie Lian, Jingyu Liu, Shu Zhang +4
Instance level detection and segmentation of thoracic diseases or abnormalities are crucial for automatic diagnosis in chest X-ray images. Leveraging on constant structure and dise…