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20202025
most citedDynamic Image for 3D MRI Image Alzheimer's Disease Classification

34 citations · 70 across the 10 of their papers we have counts for

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9 papers · 1 filter

cs.CV2025

Beta Distribution Learning for Reliable Roadway Crash Risk Assessment

Ahmad Elallaf, Nathan Jacobs, Xinyue Ye +2

Roadway traffic accidents represent a global health crisis, responsible for over a million deaths annually and costing many countries up to 3% of their GDP. Traditional traffic saf…

cs.CV20213 cited

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…

cs.CV2020

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…

cs.CV202034 cited

Dynamic Image for 3D MRI Image Alzheimer's Disease Classification

Xin Xing, Gongbo Liang, Hunter Blanton +4

We propose to apply a 2D CNN architecture to 3D MRI image Alzheimer's disease classification. Training a 3D convolutional neural network (CNN) is time-consuming and computationally…

cs.CV202029 cited

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…

cs.CV2020

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…