SAGCNet: Spatial-Aware Graph Completion Network for Missing Slice Imputation in Population CMR Imaging

Magnetic resonance imaging (MRI) provides detailed soft-tissue characteristics that assist in disease diagnosis and screening. However, the accuracy of clinical practice is often hindered by missing or unusable slices due to various factors. Volumetric MRI synthesis methods have been developed to address this issue by imputing missing slices from available ones. The inherent 3D nature of volumetric MRI data, such as cardiac magnetic resonance (CMR), poses significant challenges for missing slice imputation approaches, including (1) the difficulty of modeling local inter-slice correlations and dependencies of volumetric slices, and (2) the limited exploration of crucial 3D spatial information and global context. In this study, to mitigate these issues, we present Spatial-Aware Graph Completion Network (SAGCNet) to overcome the dependency on complete volumetric data, featuring two main innovations: (1) a volumetric slice graph completion module that incorporates the inter-slice relationships into a graph structure, and (2) a volumetric spatial adapter component that enables our model to effectively capture and utilize various forms of 3D spatial context. Extensive experiments on cardiac MRI datasets demonstrate that SAGCNet is capable of synthesizing absent CMR slices, outperforming competitive state-of-the-art MRI synthesis methods both quantitatively and qualitatively. Notably, our model maintains superior performance even with limited slice data.

Legal Fact Prediction: Task Definition and Dataset Construction

Legal facts refer to the facts that can be proven by acknowledged evidence in a trial. They form the basis for the determination of court judgments. This paper introduces a novel NLP task: legal fact prediction, which aims to predict the legal fact based on a list of evidence. The predicted facts can instruct the parties and their lawyers involved in a trial to strengthen their submissions and optimize their strategies during the trial. Moreover, since real legal facts are difficult to obtain before the final judgment, the predicted facts also serve as an important basis for legal judgment prediction. We construct a benchmark dataset consisting of evidence lists and ground-truth legal facts for real civil loan cases, LFPLoan. Our experiments on this dataset show that this task is non-trivial and requires further considerable research efforts.