12 accepted papers
Though machine learning algorithms are able to achieve pattern recognition from the correlation between data and labels, the presence of spurious features in the data decreases the robustness of these learned relationships with respect to varied testing environments. This is known as out-of-distribu…
Despite the empirical success in various domains, it has been revealed that deep neural networks are vulnerable to maliciously perturbed input data that much degrade their performance. This is known as adversarial attacks. To counter adversarial attacks, adversarial training formulated as a form of…
In Robot-Assisted Minimally Invasive Surgery (RAMIS), a camera assistant is normally required to control the position and the zooming ratio of the laparoscope, following the surgeon’s instructions. However, moving the laparoscope frequently may lead to unstable and suboptimal views, while the adjust…
Venipuncture is an indispensable procedure for both diagnosis and treatment. In this paper, unlike existing solutions that fully or partially rely on professional assistance, a compact robotic system integrating both novel hardware and software developments is introduced. The hardware consists of a…
Deep Convolutional Neural Networks (DCNNs) are used extensively in medical image segmentation and hence 3D navigation for robot-assisted Minimally Invasive Surgeries (MISs). However, current DCNNs usually use down sampling layers for increasing the receptive field and gaining abstract semantic infor…
Accurate volume segmentation from the Computed Tomography (CT) scan is a common prerequisite for pre-operative planning, intra-operative guidance and quantitative assessment of therapeutic outcomes in robot-assisted Minimally Invasive Surgery (MIS). 3D Deep Convolutional Neural Network (DCNN) is a v…
Two-dimensional (2-D) biomedical semantic segmentation is important for robotic vision in surgery. Segmentation methods based on deep convolutional neural network (DCNN) can out-perform conventional methods in terms of both accuracy and levels of automation. One common issue in training a DCNN for b
In fenestrated endovascular aortic repair (FEVAR), accurate alignment of stent graft fenestrations or scallops with aortic branches is essential for establishing complete blood flow perfusion. Current navigation is largely based on two-dimensional (2-D) fluoroscopic images, which lacks 3-D anatomica
The current standard of intra-operative navigation during Fenestrated Endovascular Aortic Repair (FEVAR) calls for the need of 3D alignments between inserted devices and aortic branches. The navigation commonly via 2D fluoroscopic images, lacks anatomical information, resulting in longer operation h…
Robot-assisted deployment of fenestrated stent grafts in fenestrated endovascular aortic repair (FEVAR) requires accurate geometrical alignment. Currently, this process is guided by two-dimensional (2-D) fluoroscopy, which is insufficiently informative and error prone. In this letter, a real-time fr
Robot-assisted Fenestrated Endovascular Aortic Repair (FEVAR) is currently navigated by 2D fluoroscopy which is insufficiently informative. Previously, a semi-automatic 3D shape instantiation method was developed to instantiate the 3D shape of a main, deployed, and fenestrated stent graft from a sin…
Radiofrequency Catheter Ablation (RFCA) is a procedure used to treat cardiac arrhythmias by burning at regions of the endocardial walls to prevent the abnormal electrical circuits causing the problem. Patients with Adult Congenital Heart Disease (ACHD) who have undergone surgical treatments suffer s