About Us

Our Mission

CuraCloud’s mission is to collaboratively develop medical AI solutions with healthcare delivery organizations, medical technology leaders, and pharmaceutical companies to improve diagnostics, care processes, and clinical outcomes.

CuraCloud is an international medical AI R&D company developing software medical devices and providing professional services to medical technology vendors, healthcare organizations, and pharmaceutical companies. Our machine learning scientists, software engineers, and designers collaborate with leading medical centers around the world to apply the latest advances in machine learning, medical informatics, and regulatory science to improve human health.

Since 2016, we have been applying our AI expertise to some of the most challenging problems in healthcare. Many of our team members worked together for over ten years before starting CuraCloud, developing advanced technologies for leading companies around the world.

Today, we work closely with our clinical collaboration partners to create scalable and compliant medical solutions. We also work with in-house clinicians and external radiology advisers to help us navigate the clinical workflow and define clinical use cases.

Leadership Team

Youbing Yin, PhD

VP of R&D

Edmund Butler

VP Corporate Development

Kunlin Cao, PhD

VP of Imaging Analytics

Rachel Gerson, MD

Radiology Advisor

Xiaoxiao Liu, PhD

Director of Products & Services

Selected Publications

NEW Learn to be Uncertain: Leveraging Uncertain Labels in Chest X-rays with Bayesian Neural Networks

Yang HY, Yang J, Pan Y, Cao K, Song Q, et al.,
Uncertainty and Robustness in Deep Visual Learning Workshop, IEEE Conference on Computer Vision and Pattern Recognition, June, 2019.

NEW Precise Diagnosis of Intracranial Hemorrhage and Subtypes Using a Three-dimensional Joint Convolutional and Recurrent Neural Network

Ye H*, Gao F* (*Equal contribution), Yin Y, Guo D, Zhao P, et al.,
European Radiology, March, 2019.

NEW DeepCenterline: a Multi-task Fully Convolutional Network for Centerline Extraction

Guo Z, Bai J, Lu Y, Wang X, Cao K, et al.,
International conference on Information Processing in Medical Imaging (IPMI), 2019.

Residual Attention Based Network for Hand Bone Age Assessment

Wu E, Kong B, Wang X, Bai J, Lu Y, et al.,
IEEE International Symposium on Biomedical Imaging (ISBI), 2019.

Dual Adversarial Autoencoder for Dermoscopic Image Generative Modeling

Yang, HY, Staib, L.,
IEEE International Symposium on Biomedical Imaging (ISBI), 2019.

Automated Anatomical Labeling of Coronary Arteries via Bidirectional Tree LSTMs

Wu D, Wang X, Bai J, Xu X, Ouyang B, et al.
International Journal of Computer Assisted Radiology and Surgery, 2019.14: 271.

Automatic Brain Tumor Segmentation with Contour Aware Residual Network and Adversarial Training

Yang, HY. Yang, J.,
International MICCAI Brainlesion Workshop, 2018.

Volumetric Adversarial Training for Ischemic Stroke Lesion Segmentation

Yang, HY
International MICCAI Brainlesion Workshop, 2018.

Train a 3D U-Net to Segment Cranial Vasculature in CTA Volume without Manual Annotation

Chen X, Lu Y, Bai J, Yin Y, Cao K, Li Y, et al.,
International Symposium on Biomedical Imaging (ISBI),  2018.

Integrate Domain Knowledge in Training CNN for Ultrasonography Breast Cancer Diagnosis

Liu J, Li W, Zhao N, Cao K, Yin Y et al.,
International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), 2018.

Invasive Cancer Detection Utilizing Compressed Convolutional Neural Network and Transfer Learning

Kong B, Sun S, Wang X, Song Q, Zhang S.
International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), 2018.

A Novel Method of Estimating Small Airway Disease Using Inspiratory-to-Expiratory Computed Tomography

Kirby M, Yin Y, et al.,
International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), 2018.

Integrate Domain Knowledge in Training CNN for Ultrasonography Breast Cancer Diagnosis

Liu J, Li W, Zhao N, Cao K, Yin Y, et al.,
Proc of Medical Image Computing and Computer Assisted Intervention, 2018 .

Risk Stratification of Lung Nodules Using 3D CNN-Based Multi-task Learning

Hussein S, Cao K, Song Q, Bagci U
Information Processing in Medical Imaging (IPMI),  2017 .

Cancer Metastasis Detection via Spatially Structured Deep Network

Kong B*, Wang X* (*Equal contribution), Li Z, Song Q, Zhang S.
International Conference on Information Processing in Medical Imaging (IPMI),  2017.

A computational model for the automatic diagnosis of attention deficit hyperactivity disorder based on functional brain volume

Tan L, Guo X, Ren S, Epstein, J, Lu L
Frontiers in computational neuroscience, 11, 75. 2017.

Towards Quantitative Assessment of Rheumatoid Arthritis Using Volumetric Ultrasound

Cao K, Mills DM, Thiele RG, Patwardhan KA
IEEE Transactions on Biomedical Engineering (TBME), 63(2): 449-458, 2016.

MASCG: Multi-Atlas Segmentation Constrained Graph Method for Accurate Segmentation of Hip CT images

Chu C, Bai J, Wu X, Zheng G.
Medical Image Analysis, 26(1):173, December 2015.

Multiple Surface Segmentation Using Truncated Convex Priors

Shah A, Bai J, Hu Z, Sadda S, Wu X
Medical Image Computing and Computer-Assisted Intervention (MICCAI), 2015.

Computed tomography predictors of response to endobronchial valve lung reduction treatment. Comparison with Chartis

Schuhmann M, RaffyP, Yin Y, Gompelmann D, Oguz I et al.,
American Journal of Respiratory and Critical Care Medicine (AJRCCM), Vol. 191 (7), 767-774, 2015.

Error-tolerant Scribbles Based Interactive Image Segmentation

Bai J, Wu X
Computer Vision and Pattern Recognition (CVPR), 2014 .

Globally Optimal Lung Tumor Co-segmentation of 4D CT and PET Images

Bai J, Song Q, Bhatia S, Wu X
Proceedings of SPIE Medical Imaging (oral presentation), 2013 .

Optimal Co-segmentation of Tumor in PET-CT Images with Context Information

Song Q, Bai J, Han D, Bhatia S, Sun W, et al.,
IEEE Transactions on Medical Imaging, 32(9):1685-97, September 2013.

Intensity-based Registration for Lung Motion Estimation

Cao K, Ding K, Amelon R, Du K, Reinhardt J et al.,
In Springer Book on “4D Modeling and Estimation of Respiratory Motion for Radiation Therapy”, published in the Springer series Biological and Medical Physics, Biomedical Engineering 2013, pp 125-158.

Motion-Compensated Mega-Voltage Cone Beam CT Using the Deformation Derived Directly from 2D Projection Images

Chen M, Cao K,  Zheng Y, C.Siochi R
IEEE Transactions on Medical Imaging, 32(8): 1365-1375, 2013 .

Fast Dynamic Programming for Labeling Problems with Ordering Constraints

Bai J, Song Q, Veksler O, Wu X
Computer Vision and Pattern Recognition (CVPR), 2012.

Registration-based estimates of local lung tissue expansion compared to xenon-CT measures of specific ventilation

Reinhardt J, Ding K, Cao K, Christensen G, Hoffman E et al.,
Medical Image Analysis, 12(6):752-763, 2008 .

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