29 citations · 35 across the 8 of their papers we have counts for
8 papers
Estimating Cluster Masses from SDSS Multi-band Images with Transfer Learning
Sheng-Chieh Lin, Yuanyuan Su, Gongbo Liang +3
The total masses of galaxy clusters characterize many aspects of astrophysics and the underlying cosmology. It is crucial to obtain reliable and accurate mass estimates for numerou…
Dynamic Feature Alignment for Semi-supervised Domain Adaptation
Yu Zhang, Gongbo Liang, Nathan Jacobs
Most research on domain adaptation has focused on the purely unsupervised setting, where no labeled examples in the target domain are available. However, in many real-world scenari…
Optical Wavelength Guided Self-Supervised Feature Learning For Galaxy Cluster Richness Estimate
Gongbo Liang, Yuanyuan Su, Sheng-Chieh Lin +3
Most galaxies in the nearby Universe are gravitationally bound to a cluster or group of galaxies. Their optical contents, such as optical richness, are crucial for understanding th…
Improved Trainable Calibration Method for Neural Networks on Medical Imaging Classification
Gongbo Liang, Yu Zhang, Xiaoqin Wang +1
Recent works have shown that deep neural networks can achieve super-human performance in a wide range of image classification tasks in the medical imaging domain. However, these wo…
Unsupervised Domain Adaptation for Mammogram Image Classification: A Promising Tool for Model Generalization
Yu Zhang, Gongbo Liang, Nathan Jacobs +1
Generalization is one of the key challenges in the clinical validation and application of deep learning models to medical images. Studies have shown that such models trained on pub…
Joint 2D-3D Breast Cancer Classification
Gongbo Liang, Xiaoqin Wang, Yu Zhang +4
Breast cancer is the malignant tumor that causes the highest number of cancer deaths in females. Digital mammograms (DM or 2D mammogram) and digital breast tomosynthesis (DBT or 3D…