1k citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Shanghai Jiao Tong UniversityCN6 papers
- Shanghai Chest HospitalCN2 papers
- University of California, IrvineUS2 papers
- Boston CollegeUS1 paper
- École Polytechnique Fédérale de LausanneCH1 paper
- Fudan UniversityCN1 paper
- Guangdong Academy of Medical SciencesCN1 paper
- Hangzhou Cancer HospitalCN1 paper
- Harvard University PressUS1 paper
- Kunming Medical UniversityCN1 paper
- Lenovo (China)CN1 paper
- Nanjing University of Science and TechnologyCN1 paper
6 papers
Prototype Learning Guided Hybrid Network for Breast Tumor Segmentation in DCE-MRI
Lei Zhou, Yuzhong Zhang, Jiadong Zhang +8
Automated breast tumor segmentation on the basis of dynamic contrast-enhancement magnetic resonance imaging (DCE-MRI) has shown great promise in clinical practice, particularly for…
Differentiable Projection from Optical Coherence Tomography B-Scan without Retinal Layer Segmentation Supervision
Dingyi Rong, Jiancheng Yang, Bingbing Ni +1
Projection map (PM) from optical coherence tomography (OCT) B-scan is an important tool to diagnose retinal diseases, which typically requires retinal layer segmentation. In this s…
MedMNIST v2 -- A large-scale lightweight benchmark for 2D and 3D biomedical image classification
Jiancheng Yang, Rui Shi, Donglai Wei +5
We introduce MedMNIST v2, a large-scale MNIST-like dataset collection of standardized biomedical images, including 12 datasets for 2D and 6 datasets for 3D. All images are pre-proc…
Learning Tumor Growth via Follow-Up Volume Prediction for Lung Nodules
Yamin Li, Jiancheng Yang, Yi Xu +5
Follow-up serves an important role in the management of pulmonary nodules for lung cancer. Imaging diagnostic guidelines with expert consensus have been made to help radiologists m…
AttentionAnatomy: A unified framework for whole-body organs at risk segmentation using multiple partially annotated datasets
Shanlin Sun, Yang Liu, Narisu Bai +5
Organs-at-risk (OAR) delineation in computed tomography (CT) is an important step in Radiation Therapy (RT) planning. Recently, deep learning based methods for OAR delineation have…
AnatomyNet: Deep Learning for Fast and Fully Automated Whole-volume Segmentation of Head and Neck Anatomy
Wentao Zhu, Yufang Huang, Liang Zeng +6
Methods: Our deep learning model, called AnatomyNet, segments OARs from head and neck CT images in an end-to-end fashion, receiving whole-volume HaN CT images as input and generati…