An Inductive Reasoning Model based on Interpretable Logical Rules over temporal knowledge graph.

Extrapolating future events based on historical information in temporal knowledge graphs (TKGs) holds significant research value and practical applications. In this field, the methods currently utilized can be classified as either embedding-based or logical rule-based. Embedding-based methods depend on learned entity and relation embeddings for prediction, but they suffer from the lack of interpretability due to the opaque reasoning process. On the other hand, logical rule-based methods face scalability challenges as they heavily rely on predefined logical rules. To overcome these limitations, we propose a hybrid model that combines embedding-based and logical rule-based methods to capture deep causal logic. Our model, called the Inductive Reasoning Model based on Interpretable Logical Rule (ILR-IR), aims to provide interpretable insights while effectively predicting future events in TKGs. ILR-IR delves into historical information, extracting valuable insights from logical rules embedded within relations and interaction preferences between entities. By considering both logical rules and interaction preferences, ILR-IR offers a comprehensive perspective for predicting future events. In addition, we propose the incorporation of a one-class augmented matching loss during optimization, which serves to enhance performance of the model during training. We evaluate ILR-IR on multiple datasets, including ICEWS14, ICEWS0515, and ICEWS18. Experimental results demonstrate that ILR-IR outperforms state-of-the-art baselines, showcasing its superior performance in TKG extrapolation reasoning. Moreover, ILR-IR demonstrates remarkable generalization capabilities, even when applied to related datasets that share a common relation vocabulary. This suggests that our proposed model exhibits robust zero-shot reasoning abilities. For interested parties, we have made our code publicly available at https://github.com/mxadorable/ILR-IR.

Learning to Summarize Chinese Radiology Findings With a Pre-Trained Encoder.

Automatic radiology report summarization has been an attractive research problem towards computer-aided diagnosis to alleviate physicians' workload in recent years. However, existing methods for English radiology report summarization using deep learning techniques cannot be directly applied to Chinese radiology reports due to limitations of the related corpus. In response to this, we propose an abstractive summarization approach for Chinese chest radiology report. Our approach involves the construction of a pre-training corpus using a Chinese medical-related pre-training dataset, and the collection of Chinese chest radiology reports from Department of Radiology at the Second Xiangya Hospital as the fine-tuning corpus. To improve the initialization of the encoder, we introduce a new task-oriented pre-training objective called Pseudo Summary Objective on the pre-training corpus. We then develop a Chinese pre-trained language model called Chinese medical BERT (CMBERT), which is used to initialize the encoder and fine-tuned on the abstractive summarization task. In testing our approach on a real large-scale hospital dataset, we observe that the performance of our proposed approach achieves outstanding improvement compared with other abstractive summarization models. This highlights the effectiveness of our approach in addressing the limitations of previous methods for Chinese radiology report summarization. Overall, our proposed approach demonstrates a promising direction for the automatic summarization of Chinese chest radiology reports, offering a viable solution to alleviate physicians' workload in the field of computer-aided diagnosis.

Survey on natural language processing in medical image analysis. / 自然语言处理在医学影像分析中的应用.

Recent advancement in natural language processing (NLP) and medical imaging empowers the wide applicability of deep learning models. These developments have increased not only data understanding, but also knowledge of state-of-the-art architectures and their real-world potentials. Medical imaging researchers have recognized the limitations of only targeting images, as well as the importance of integrating multimodal inputs into medical image analysis. The lack of comprehensive surveys of the current literature, however, impedes the progress of this domain. Existing research perspectives, as well as the architectures, tasks, datasets, and performance measures examined in the present literature, are reviewed in this work, and we also provide a brief description of possible future directions in the field, aiming to provide researchers and healthcare professionals with a detailed summary of existing academic research and to provide rational insights to facilitate future research.