827 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Xi'an Jiaotong UniversityCN4 papers
- Brunel University of LondonGB2 papers
- Central South UniversityCN2 papers
- Shaanxi University of Science and TechnologyCN2 papers
- Shanxi Medical UniversityCN2 papers
- Beijing Tongren HospitalCN1 paper
- Beijing Tsinghua Chang Gung HospitalCN1 paper
- Capital Medical UniversityCN1 paper
- Chengdu University of Traditional Chinese MedicineCN1 paper
- Chinese University of Hong KongHK1 paper
- Franklin CollegeUS1 paper
- Han Dan City Eye Hospital - The Third Hospital of HandanCN1 paper
7 papers · 1 filter
VisionFM: a Multi-Modal Multi-Task Vision Foundation Model for Generalist Ophthalmic Artificial Intelligence
Jianing Qiu, Jian Wu, Hao Wei +39
We present VisionFM, a foundation model pre-trained with 3.4 million ophthalmic images from 560,457 individuals, covering a broad range of ophthalmic diseases, modalities, imaging…
CiT-Net: Convolutional Neural Networks Hand in Hand with Vision Transformers for Medical Image Segmentation
Tao Lei, Rui Sun, Xuan Wang +3
The hybrid architecture of convolutional neural networks (CNNs) and Transformer are very popular for medical image segmentation. However, it suffers from two challenges. First, alt…
Nuclei Grading of Clear Cell Renal Cell Carcinoma in Histopathological Image by Composite High-Resolution Network
Zeyu Gao, Jiangbo Shi, Xianli Zhang +6
The grade of clear cell renal cell carcinoma (ccRCC) is a critical prognostic factor, making ccRCC nuclei grading a crucial task in RCC pathology analysis. Computer-aided nuclei gr…
Multi-scale super-resolution generation of low-resolution scanned pathological images
Kai Sun, Yanhua Gao, Ting Xie +5
Background. Digital pathology has aroused widespread interest in modern pathology. The key of digitalization is to scan the whole slide image (WSI) at high magnification. The lager…
Micro-CT Synthesis and Inner Ear Super Resolution via Generative Adversarial Networks and Bayesian Inference
Hongwei Li, Rameshwara G. N. Prasad, Anjany Sekuboyina +4
Existing medical image super-resolution methods rely on pairs of low- and high- resolution images to learn a mapping in a fully supervised manner. However, such image pairs are oft…
Medical Image Segmentation Using Deep Learning: A Survey
Risheng Wang, Tao Lei, Ruixia Cui +3
Deep learning has been widely used for medical image segmentation and a large number of papers has been presented recording the success of deep learning in the field. In this paper…
Effects of annotation granularity in deep learning models for histopathological images
Jiangbo Shi, Zeyu Gao, Haichuan Zhang +4
Pathological is crucial to cancer diagnosis. Usually, Pathologists draw their conclusion based on observed cell and tissue structure on histology slides. Rapid development in machi…