10 citations · 19 across the 5 of their papers we have counts for
5 papers
CS: A Controllable and Simultaneous Synthesizer of Images and Annotations with Minimal Human Intervention
Xiaodan Xing, Jiahao Huang, Yang Nan +5
The destitution of image data and corresponding expert annotations limit the training capacities of AI diagnostic models and potentially inhibit their performance. To address such…
Robust Weakly Supervised Learning for COVID-19 Recognition Using Multi-Center CT Images
Qinghao Ye, Yuan Gao, Weiping Ding +8
The world is currently experiencing an ongoing pandemic of an infectious disease named coronavirus disease 2019 (i.e., COVID-19), which is caused by the severe acute respiratory sy…
Annealing Genetic GAN for Minority Oversampling
Jingyu Hao, Chengjia Wang, Heye Zhang +1
The key to overcome class imbalance problems is to capture the distribution of minority class accurately. Generative Adversarial Networks (GANs) have shown some potentials to tackl…
Deep Attentive Wasserstein Generative Adversarial Networks for MRI Reconstruction with Recurrent Context-Awareness
Yifeng Guo, Chengjia Wang, Heye Zhang +1
The performance of traditional compressive sensing-based MRI (CS-MRI) reconstruction is affected by its slow iterative procedure and noise-induced artefacts. Although many deep lea…
Recurrent Aggregation Learning for Multi-View Echocardiographic Sequences Segmentation
Ming Li, Weiwei Zhang, Guang Yang +5
Multi-view echocardiographic sequences segmentation is crucial for clinical diagnosis. However, this task is challenging due to limited labeled data, huge noise, and large gaps acr…