Our multidisciplinary scientific team (more than 100 employees globally) combines academic and industry experience in machine learning, medical image analysis, computer vision, building highly scalable SaaS, advanced graphical user interface design, data de-identification, image annotation and processing, clinical integration, quality control, etc. We have more than 10 Ph.D.s who have extensive research and industrial experience in building healthcare solutions. Many of them had tech leader roles in GE, Siemens before joining the team. We also have in-house clinicians and external radiology advisers to help us navigate the clinical workflow and define clinical use cases.
- Ye H*, Gao F* (*Equal contribution),Yin Y, Guo D, Zhao P, et al., Precise Diagnosis of Intracranial Hemorrhage and Subtypes Using a Three-dimensional Joint Convolutional and Recurrent Neural Network. European Radiology. doi: 10.1007/s00330-019-06163-2.
- Guo Z, et al. DeepCenterline: a Multi-task Fully Convolutional Network for Centerline Extraction. Information Processing in Medical Imaging (IPMI). 2019.
- Wu D, et al. Automated Anatomical Labeling of Coronary Arteries via Bidirectional Tree LSTM. International Journal of Computer Assisted Radiology and Surgery (IJCARS) 2019, 14: 271.
- Chen X, Lu Y, Bai J, Yin Y, Cao K, Li Y, et al. Train a 3D U-Net to Segment Cranial Vasculature in CTA Volume Without Manual Annotation. International Symposium on Biomedical Imaging (ISBI) 2018.
- Yang, HY. Yang, J. Automatic Brain Tumor Segmentation with Contour Aware Residual Network and Adversarial Training. International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI) Brainlesion Workshop, 2018.
- Yang, HY. Volumetric Adversarial Training for Ischemic Stroke Lesion Segmentation. International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI) Brainlesion Workshop, 2018.
- Liu, J., et al. Integrate Domain Knowledge in Training CNN for Ultrasonography Breast Cancer Diagnosis. International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), 2018.
- Kong, B., et al. Invasive Cancer Detection Utilizing Compressed Convolutional Neural Network and Transfer Learning. International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), 2018.
- Hussein S, Cao K, Song Q, Bagci U. Risk Stratification of Lung Nodules Using 3D CNN-Based Multi-task Learning. Information Processing in Medical Imaging (IPMI). 2017.
- Kong B, Wang X, Li Z, Song Q, Zhang S. Cancer Metastasis Detection Via Spatially Structured Deep Network. Information Processing in Medical Imaging (IPMI). 2017.
- Cao K, Mills DM, Thiele RG, Patwardhan KA. Toward Quantitative Assessment of Rheumatoid Arthritis Using Volumetric Ultrasound. IEEE Trans Biomed Eng. 2016.
- Wang X, Chang M-C, Ying Y, Lyu S. Co-Regularized PLSA for Multi-Modal Learning. Proc 30th Association for the Advancement of Artificial Intelligence (AAAI) Conf Artif Intell. 2016.
- Xu P, Liu X, Song Q, Chen G, Wang D, Zhang H, et al. Patient-specific Structural Effects on Hemodynamics in the Ischemic Lower Limb Artery. Sci Rep.2016
- Bai J, Wu X. Error-tolerant Scribbles Based Interactive Image Segmentation. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR). 2014.
- 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.
- Lyu S, Wang X. On Algorithms for Sparse Multi-factor NMF. Neural Information Processing Systems (NIPS). 2013.
- Chen M, Cao K, Zheng Y, Siochi RAC. Motion-compensated Mega-voltage Cone Beam CT Using the Deformation Derived Directly from 2D Projection Images. IEEE Trans Med Imaging. 2013.
- Bai J, Song Q, Veksler O, Wu X. Fast Dynamic Programming for Labeling Problems with Ordering Constraints. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR). 2012.
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