X-ray image based medical report generation achieves significant progress in recent years with the help of large language models, however, these models have not fully exploited the effective information in visual image regions, resulting in reports that are linguistically sound but insufficient in describing key diseases. In this paper, we propose a novel associative memory-enhanced X-ray report generation model that effectively mimics the process of professional doctors writing medical reports. It considers both the mining of global and local visual information and associates historical report information to better complete the writing of the current report. Specifically, given an X-ray image, we first utilize a classification model along with its activation maps to accomplish the mining of visual regions highly associated with diseases and the learning of disease query tokens. Then, we employ a visual Hopfield network to establish memory associations for disease-related tokens, and a report Hopfield network to retrieve report memory information. This process facilitates the generation of high-quality reports based on a large language model and achieves state-of-the-art performance on multiple benchmark datasets, including the IU X-ray, MIMIC-CXR, and Chexpert Plus. The source code and pre-trained models of this work have been released on https://github.com/Event-AHU/Medical_Image_Analysis.

The task of chest X-ray report generation, which aims to simulate the diagnosis process of doctors, has received widespread attention. Compared with the image caption task, chest X-ray report generation is more challenging since it needs to generate a longer and more accurate description of each diagnostic part in chest X-ray images. Most of existing works focus on how to extract better visual features or more accurate text expression based on existing reports. However, they ignore the interactions between visual and text modalities and are thus obviously not in line with human thinking. A small part of works explore the interactions of visual and text modalities, but data-driven learning of cross-modal information mapping can not break the semantic gap between different modalities. In this work, we propose a novel approach called Knowledge-guided Cross-modal Alignment and Progressive fusion (KCAP), which takes the knowledge words from a created medical knowledge dictionary as the bridge to guide the cross-modal feature alignment and fusion, for accurate chest X-ray report generation. In particular, we create the medical knowledge dictionary by extracting medical phrases from the training set and then selecting some phrases with substantive meanings as knowledge words based on their frequency of occurrence. Based on the knowledge words from the medical knowledge dictionary, the visual and text modalities are interacted by a mapping layer for the enhancement of the features of two modalities, and then the alignment fusion module is introduced to mitigate the semantic gap between visual and text modalities. To retain the important details of the original information, we design a progressive fusion scheme to integrate the advantages of both salient fused and original features to generate better medical reports. The experimental results on IU-Xray and MIMIC datasets demonstrate the effectiveness of the proposed KCAP.

Learning based feature matching methods have been commonly studied in recent years. The core issue for learning feature matching is to how to learn (1) discriminative representations for feature points (or regions) within each intra-image and (2) consensus representations for feature points across inter-images. Recently, self- and cross-attention models have been exploited to address this issue. However, in many scenes, features are coming with large-scale, redundant and outliers contaminated. Previous self-/cross-attention models generally conduct message passing on all primal features which thus lead to redundant learning and high computational cost. To mitigate limitations, inspired by recent seed matching methods, in this article, we propose a novel efficient Anchor Matching Transformer (AMatFormer) for the feature matching problem. AMatFormer has two main aspects: First, it mainly conducts self-/cross-attention on some anchor features and leverages these anchor features as message bottleneck to learn the representations for all primal features. Thus, it can be implemented efficiently and compactly. Second, AMatFormer adopts a shared FFN module to further embed the features of two images into the common domain and thus learn the consensus feature representations for the matching problem. Experiments on several benchmarks demonstrate the effectiveness and efficiency of the proposed AMatFormer matching approach.

With the exacerbation of bridge aging issues, the demand for efficient and cost-effective bridge inspection solutions becomes increasingly urgent. Currently, most methods for surface damage detection on bridges employ single-modal, image-based object detection models. Despite their overall effectiveness on specific datasets, such methods frequently encounter error detection issues during actual inspection processes. This study proposes a multimodal (image and text) model for surface damage detection in bridge structures by combining the CLIP model with the YOLOv8 model. By conducting tests on the composite concrete bridge of the Jiaozhou Bay Bridge, Qingdao, the effectiveness of bridge detection using drones is validated, and the common issue of false positive detections in traditional object detection models is successfully addressed.