CuraCloud™ is a venture-backed company formed to accelerate the use of AI to improve healthcare efficiency and precision through better diagnostics. Our founding team formed the company in early 2016, having previously designed AI features for some of the worlds most advanced diagnostic imaging equipment manufacturers.
Today our multidisciplinary R&D team includes more than 20 PhDs from leading US universities with field experience in machine learning, deep learning, computer vision, medical image analysis, genomic sequencing, and molecular biology. Our team members have published over 100 articles in peer-reviewed science journals, and have accumulated decades of experience working with healthcare delivery systems world-wide.
The future of smarter healthcare is artificial intelligence. CuraCloud has attracted strong financial backing because it leads the field in using the power of machine learning to enhance healthcare effectiveness, precision and efficiency worldwide.
CuraCloud investors bring an unmatched breadth and depth of experience in introducing disruptive technologies across global markets. They see outstanding growth potential in healthcare markets worldwide, and regard CuraCloud’s technology as a key ingredient for improving and expanding access to high-quality healthcare across the globe.
CuraCloud is actively seeking collaborators to achieve smarter healthcare through machine intelligence. Examples of our collaborations:
- Radiology workflow software company: Clario Medical is collaborating with CuraCloud to use AI to prioritize radiology worklists, so that the most urgent exams are seen first.
- Cancer center: A cancer research center is working with us to use Natural Language Processing to extract data from unstructured clinical documents in order to populate a cancer registry.
- Academic research: Multiple groups are collaborating with CuraCloud to better detect circulating tumor cell DNA to aid cancer detection.
Our multidisciplinary scientific team has published well over 100 articles in leading journals, textbooks, and proceedings of international scientific conferences, even though our major focus is product development.
A sample of our thought leadership:
- Chen X, Lu Y, Bai J, Yin Y, Cao K, Li Y, Chen H, Song Q, Wu J. “Train a 3D U-Net to Segment Cranial Vasculature in CTA Volume without Manual Annotation”. International Symposium on Biomedical Imaging (ISBI) 2018.
- Hussein, K. Cao,Q. Song, and U. Bagci, “Risk Stratification of Lung Nodules Using 3D CNN-Based Multi-task Learning”, Information Processing in Medical Imaging (IPMI)2017
- Bin Kong, Xin Wang, Zhongyu Li, Qi Song, Shaoting Zhang: Cancer Metastasis Detection via Spatially Structured Deep Network. The 25th Biennial International Conference on Information Processing in Medical Imaging, 2017.
- Wang, X. Chang, S. Lyu. Co-regularized PLSA For Multi-Modal Learning. In: AAAI Conference on Artificial Intelligence (AAAI), Phoenix, Arizona USA, 2016.
- K. Cao, D. Mills, R. Thiele, and K. Padwardhan, “Towards Quantitative Assessment of Rheumatoid Arthritis Using Volumetric Ultrasound”, IEEE Transactions on Biomedical Engineering (TBME), 63(2): 449-458, 2016
- Bai, J. and Wu, X. Error-tolerant scribbles based interactive image segmentation. In Computer Vision and Pattern Recognition (CVPR), 2014 IEEE Conference on (pp. 392-399). (2014)
- Lyu S, Wang X. On algorithms for sparse multi-factor NMF. Advances in Neural Information Processing Systems (NIPS), 2013.
- M. Chen, K. Cao, Y. Zheng and R. A. Siochi, “Motion-Compensated Mega-Voltage Cone Beam CT Using the Deformation Derived Directly from 2D Projection Images”, IEEE Transactions on Medical Imaging (TMI), 32(8): 1365-1375, 2013
- Cao,K. et al., Chapter 7 “Intensity-based Registration for Lung Motion Estimation”, In Jan Ehrhardt and Cristian Lorenz, editors, 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 (book chapter)
- Bai, J., Song, Q., Veksler, O. and Wu, X. Fast dynamic programming for labeling problems with ordering constraints. In Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on (pp. 1728-1735). (2012)
- Zhou J, Wan J, Gao X, Zhang X, Jaffrey SR, Qian SB. Dynamic m(6)A mRNA methylation directs translational control of heat shock response. Nature. 2015 Oct 22;526(7574):591-4