Semantic Parsing with Dual Learning

Semantic parsing converts natural language queries into structured logical forms. The paucity of annotated training samples is a fundamental challenge in this field. In this work, we develop a semantic parsing framework with the dual learning algorithm, which enables a semantic parser to make full use of data (labeled and even unlabeled) through a dual-learning game. This game between a primal model (semantic parsing) and a dual model (logical form to query) forces them to regularize each other, and can achieve feedback signals from some prior-knowledge. By utilizing the prior-knowledge of logical form structures, we propose a novel reward signal at the surface and semantic levels which tends to generate complete and reasonable logical forms. Experimental results show that our approach achieves new state-of-the-art performance on ATIS dataset and gets competitive performance on Overnight dataset.Comment: Accepted by ACL 2019 Long Pape

A Type II Radio Burst Driven by a Blowout Jet on the Sun

Type II radio bursts are often associated with coronal shocks that are typically driven by coronal mass ejections (CMEs) from the Sun. Here, we conduct a case study of a type II radio burst that is associated with a C4.5 class flare and a blowout jet, but without the presence of a CME. The blowout jet is observed near the solar disk center in the extreme-ultraviolet (EUV) passbands with different characteristic temperatures. Its evolution involves an initial phase and an ejection phase with a velocity of 560 km/s. Ahead of the jet front, an EUV wave propagates at a projected velocity of 403 km/s in the initial stage. The moving velocity of the source region of the type II radio burst is estimated to be 641 km/s, which corresponds to the shock velocity against the coronal density gradient. The EUV wave and the type II radio burst are closely related to the ejection of the blowout jet, suggesting that both are likely the manifestation of a coronal shock driven by the ejection of the blowout jet. The type II radio burst likely starts lower than those associated with CMEs. The combination of the velocities of the radio burst and the EUV wave yields a modified shock velocity at 757 km/s. The Alfven Mach number is in the range of 1.09-1.18, implying that the shock velocity is 10%-20% larger than the local Alfven velocity.Comment: Accepted by ApJ, 17 pages, and 6 figure

DisAsymNet: Disentanglement of Asymmetrical Abnormality on Bilateral Mammograms using Self-adversarial Learning

Asymmetry is a crucial characteristic of bilateral mammograms (Bi-MG) when abnormalities are developing. It is widely utilized by radiologists for diagnosis. The question of 'what the symmetrical Bi-MG would look like when the asymmetrical abnormalities have been removed ?' has not yet received strong attention in the development of algorithms on mammograms. Addressing this question could provide valuable insights into mammographic anatomy and aid in diagnostic interpretation. Hence, we propose a novel framework, DisAsymNet, which utilizes asymmetrical abnormality transformer guided self-adversarial learning for disentangling abnormalities and symmetric Bi-MG. At the same time, our proposed method is partially guided by randomly synthesized abnormalities. We conduct experiments on three public and one in-house dataset, and demonstrate that our method outperforms existing methods in abnormality classification, segmentation, and localization tasks. Additionally, reconstructed normal mammograms can provide insights toward better interpretable visual cues for clinical diagnosis. The code will be accessible to the public

Automated image registration of cerebral digital subtraction angiography

Purpose: Our aim is to automatically align digital subtraction angiography (DSA) series, recorded before and after endovascular thrombectomy. Such alignment may enable quantification of procedural success. Methods: Firstly, we examine the inherent limitations for image registration, caused by the projective characteristics of DSA imaging, in a representative set of image pairs from thrombectomy procedures. Secondly, we develop and assess various image registration methods (SIFT, ORB). We assess these methods using manually annotated point correspondences for thrombectomy image pairs. Results: Linear transformations that account for scale differences are effective in aligning DSA sequences. Two anatomical landmarks can be reliably identified for registration using a U-net. Point-based registration using SIFT and ORB proves to be most effective for DSA registration and are applicable to recordings for all patient sub-types. Image-based techniques are less effective and did not refine the results of the best point-based registration method. Conclusion: We developed and assessed an automated image registration approach for cerebral DSA sequences, recorded before and after endovascular thrombectomy. Accurate results were obtained for approximately 85% of our image pairs.</p

Spatio-Temporal U-Net for Cerebral Artery and Vein Segmentation in Digital Subtraction Angiography

X-ray digital subtraction angiography (DSA) is widely used for vessel and/or flow visualization and interventional guidance during endovascular treatment of patients with a stroke or aneurysm. To assist in peri-operative decision making as well as post-operative prognosis, automatic DSA analysis algorithms are being developed to obtain relevant image-based information. Such analyses include detection of vascular disease, evaluation of perfusion based on time intensity curves (TIC), and quantitative biomarker extraction for automated treatment evaluation in endovascular thrombectomy. Methodologically, such vessel-based analysis tasks may be facilitated by automatic and accurate artery-vein segmentation algorithms. The present work describes to the best of our knowledge the first study that addresses automatic artery-vein segmentation in DSA using deep learning. We propose a novel spatio-temporal U-Net (ST U-Net) architecture which integrates convolutional gated recurrent units (ConvGRU) in the contracting branch of U-Net. The network encodes a 2D+t DSA series of variable length and decodes it into a 2D segmentation image. On a multi-center routinely acquired dataset, the proposed method significantly outperformed U-Net (P<0.001) and traditional Frangi-based K-means clustering (P$<$0.001). Particularly in artery-vein segmentation, ST U-Net achieved a Dice coefficient of 0.794, surpassing the existing state-of-the-art methods by a margin of 12\%-20\%. Code will be made publicly available upon acceptance