At CuraCloud™ we are committed to improving healthcare by applying AI to advanced diagnostics and precision therapeutics that can be delivered on a global scale. Our development efforts are driven by a vision that unites genomics, immunology, and medical image analysis into a comprehensive range of services supporting medical professionals, patients, and medical researchers.
Our multidisciplinary scientific team combines academic and industry experience in machine learning, computer vision, medical image analysis, next generation sequencing, and immunotherapy research. Our team members have published over 100 articles in peer-reviewed science journals.
CuraCloud is collaborating with leading healthcare delivery organizations to bring these advanced technologies into clinical practice.
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: We are working with PACS vendors and with a radiology worklist companies to launch medical image analysis applications from within the clinical workflow to improve productivity and quality.
- Radiology Group Practices: We are working with private radiology practices to assess the clinical performance of our medical image analysis applications and to determine its benefits.
- 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
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