## 1 Introduction
Moreover, MERs are similar to some business documents in that table bodies usually have lists of inspection items, where each  
Figure 1: **(a)** An example of a Chinese medical examination report document; **(b)** example of the result of the table structure reconstruction for table body only; **(c)** example of the final extracted result of the method in this paper. item has a set of key information; it is challenging to rely on multimodal feature fusion alone to achieve the association of this key information within table bodies. To address the existing problems, this paper proposes an information structure extraction method named MSIE-Net, and it is applied to the Chinese medical examination report dataset (CMERD) for validation. The overview of this method is demonstrated in Figure 2; it consists of four parts: optical character recognition (OCR), key information extraction (KIE), associated entity detection, and structured post-processing. Notably, OCR, as the upstream task of KIE, is not the main work of this study. However, to minimize the impact of its results on the subsequent KIE stage, a well-established OCR algorithm is adopted for training on CMERD, aiming at obtaining the most accurate OCR results possible. This research focuses on the other three stages. The KIE stage uses the text box coordinates and text content output from the OCR sub-module to identify entity categories and explore their possible entity relationships through semantic entity recognition (SER) and relationship extraction (RE) techniques. The associated entity detection stage directly takes the image as input and accurately obtains the coordinate information of the table body line items with the help of object detection techniques. Finally, the structured post-processing stage aggregates and organizes the coordinates of the output information from the first two stages to form a structured output format for further study, as shown in Figure 1c. Through this series of processing flows, MSIE-Net can realize the comprehensive and structured extraction of medical examination report information, which provides powerful support for research in related fields. The contributions of this paper are as follows:  
* We introduce MSIE-Net for structured information extraction from MERs. Experiments have shown its superior ability to provide more complete information and more structurally organized output compared to existing methods for extracting examination report information. * We propose an attention refinement module that augments traditional self-attention mechanisms. The module uses information from previously parsed regions to adjust the attention score of the current input, focusing on yet unparsed regions and, thus, enhancing the model's contextual comprehension. * We introduce the intra-layer feature adjustment module (IFAM), designed for detecting MER-associated entities, to fuse global and local features. This addresses prevalent issues like omitted rows and inaccurate edge demarcation. * For the CMERD images constructed in this study, we devised an exhaustive annotation scheme. Apart from the pixel location, the annotation of the dataset includes the content and entity class of the text, the entity class relationship of the \"Header\", and the position of the smallest bounding rectangle of all entities in each row of the \"Body\". Figure 2: Overall flow diagram of structured information extraction. Among them, DBNet++ [14] is used for text detection and CRNN [15] is used for text recognition.