Weixin Si

Bio: Weixin Si is an academic researcher from Chinese Academy of Sciences. The author has contributed to research in topics: Segmentation & Computer science. The author has an hindex of 9, co-authored 44 publications receiving 390 citations. Previous affiliations of Weixin Si include The Chinese University of Hong Kong & Shenzhen University.

Federated deep learning for detecting COVID-19 lung abnormalities in CT: a privacy-preserving multinational validation study

Abstract: Data privacy mechanisms are essential for rapidly scaling medical training databases to capture the heterogeneity of patient data distributions toward robust and generalizable machine learning systems. In the current COVID-19 pandemic, a major focus of artificial intelligence (AI) is interpreting chest CT, which can be readily used in the assessment and management of the disease. This paper demonstrates the feasibility of a federated learning method for detecting COVID-19 related CT abnormalities with external validation on patients from a multinational study. We recruited 132 patients from seven multinational different centers, with three internal hospitals from Hong Kong for training and testing, and four external, independent datasets from Mainland China and Germany, for validating model generalizability. We also conducted case studies on longitudinal scans for automated estimation of lesion burden for hospitalized COVID-19 patients. We explore the federated learning algorithms to develop a privacy-preserving AI model for COVID-19 medical image diagnosis with good generalization capability on unseen multinational datasets. Federated learning could provide an effective mechanism during pandemics to rapidly develop clinically useful AI across institutions and countries overcoming the burden of central aggregation of large amounts of sensitive data.

3D Tracking and Positioning of Surgical Instruments in Virtual Surgery Simulation

Abstract: 3D tracking and positioning of surgical instruments is an indispensable part of virtual Surgery training system, because it is the unique interface for trainee to communicate with virtual environment. A suit of 3D tracking and positioning of surgical instruments based on stereoscopic vision is proposed. It can capture spatial movements of simulated surgical instrument in real time, and provide 6 degree of freedom information with the absolute error of less than 1 mm. The experimental results show that the 3D tracking and positioning of surgical instruments is highly accurate, easily operated, and inexpensive. Combining with force sensor and embedded acquisition device, this 3D tracking and positioning method can be used as a measurement platform of physical parameters to realize the measurement of soft tissue parameters.

Mixed Reality Guided Radiofrequency Needle Placement: A Pilot Study

Abstract: This paper presents a novel mixed reality (MR) guidance method for liver tumors radiofrequency ablation (RFA). Compared with traditional computed tomography (CT)-guided method, our system can provide a more natural and intuitive surgical mode for surgeons. In essence, our system is a holographic navigation platform, which projects a MR overlay onto the patient via HoloLens during RFA. We first reconstruct the patient-specific anatomy structure from the CT images of abdominal phantom. Then, a tailored precise registration method is employed to map the virtual-real spatial information. In addition, considering that tumor shifting during biopsy severely impacts the accuracy of RFA, our guidance system involves a motion compensation computation through data-driven physically-based modeling in holographic environment. In experiments, we conduct a user study on the comparison trial between MR-guided and CT-guided biopsy. User feedback demonstrates that our MR guidance method for needle placement procedure has the potential to simplify the operation, reduce the operation difficulty, shorten the operation time, and raise the operation precision.

Boundary Layer Theory

Abstract: The boundary layer equations for plane, incompressible, and steady flow are $$\matrix{ {u{{\partial u} \over {\partial x}} + v{{\partial u} \over {\partial y}} = - {1 \over \varrho }{{\partial p} \over {\partial x}} + v{{{\partial ^2}u} \over {\partial {y^2}}},} \cr {0 = {{\partial p} \over {\partial y}},} \cr {{{\partial u} \over {\partial x}} + {{\partial v} \over {\partial y}} = 0.} \cr }$$

Multi-Scale Self-Guided Attention for Medical Image Segmentation

Abstract: Even though convolutional neural networks (CNNs) are driving progress in medical image segmentation, standard models still have some drawbacks. First, the use of multi-scale approaches, i.e., encoder-decoder architectures, leads to a redundant use of information, where similar low-level features are extracted multiple times at multiple scales. Second, long-range feature dependencies are not efficiently modeled, resulting in non-optimal discriminative feature representations associated with each semantic class. In this paper we attempt to overcome these limitations with the proposed architecture, by capturing richer contextual dependencies based on the use of guided self-attention mechanisms. This approach is able to integrate local features with their corresponding global dependencies, as well as highlight interdependent channel maps in an adaptive manner. Further, the additional loss between different modules guides the attention mechanisms to neglect irrelevant information and focus on more discriminant regions of the image by emphasizing relevant feature associations. We evaluate the proposed model in the context of semantic segmentation on three different datasets: abdominal organs, cardiovascular structures and brain tumors. A series of ablation experiments support the importance of these attention modules in the proposed architecture. In addition, compared to other state-of-the-art segmentation networks our model yields better segmentation performance, increasing the accuracy of the predictions while reducing the standard deviation. This demonstrates the efficiency of our approach to generate precise and reliable automatic segmentations of medical images. Our code is made publicly available at: https://github.com/sinAshish/Multi-Scale-Attention .

Deep learning for cardiac image segmentation: A review

Abstract: Deep learning has become the most widely used approach for cardiac image segmentation in recent years. In this paper, we provide a review of over 100 cardiac image segmentation papers using deep learning, which covers common imaging modalities including magnetic resonance imaging (MRI), computed tomography (CT), and ultrasound (US) and major anatomical structures of interest (ventricles, atria and vessels). In addition, a summary of publicly available cardiac image datasets and code repositories are included to provide a base for encouraging reproducible research. Finally, we discuss the challenges and limitations with current deep learning-based approaches (scarcity of labels, model generalizability across different domains, interpretability) and suggest potential directions for future research.