Automated radiology report generation (RRG) holds potential to reduce radiologists' workload, especially as recent advancements in large language models (LLMs) enable the development of multimodal models for chest X-ray (CXR) report generation. However, multimodal LLMs (MLLMs) are resource-intensive, requiring vast datasets and substantial computational cost for training. T o address these challenges, we propose a retrieval-augmented generation approach that leverages multimodal retrieval and LLMs to generate radiology reports while mitigating hallucinations and reducing computational demands. Our method uses LLMs to extract key phrases from radiology reports, effectively focusing on essential diagnostic information. Through exploring effective training strategies, including image encoder structure search, adding noise to text em-beddings, and additional training objectives, we combine complementary pre-trained image encoders and adopt contrastive learning between text and semantic image embed-dings. W e evaluate our approach on MIMIC-CXR dataset, achieving state-of-the-art results on CheXbert metrics and competitive RadGraph F1 metric alongside MLLMs, without requiring LLM fine-tuning. Our method demonstrates robust generalization for multi-view RRG, making it suitable for comprehensive clinical applications.

Radiology reporting is a complex task that requires detailed image understanding, integration of multiple inputs, including comparison with prior imaging, and precise language generation. This makes it ideal for the development and use of generative multimodal models. Here, we extend report generation to include the localisation of individual findings on the image - a task we call grounded report generation. Prior work indicates that grounding is important for clarifying image understanding and interpreting AI-generated text. Therefore, grounded reporting stands to improve the utility and transparency of automated report drafting. To enable evaluation of grounded reporting, we propose a novel evaluation framework - RadFact - leveraging the reasoning capabilities of large language models (LLMs). RadFact assesses the factuality of individual generated sentences, as well as correctness of generated spatial localisations when present. We introduce MAIRA-2, a large multimodal model combining a radiology-specific image encoder with a LLM, and trained for the new task of grounded report generation on chest X-rays. MAIRA-2 uses more comprehensive inputs than explored previously: the current frontal image, the current lateral image, the prior frontal image and prior report, as well as the Indication, Technique and Comparison sections of the current report. We demonstrate that these additions significantly improve report quality and reduce hallucinations, establishing a new state of the art on findings generation (without grounding) on MIMIC-CXR while demonstrating the feasibility of grounded reporting as a novel and richer task.

Medical vision-language pre-training methods mainly leverage the correspondence between paired medical images and radiological reports. Although multi-view spatial images and temporal sequences of image-report pairs are available in off-the-shelf multi-modal medical datasets, most existing methods have not thoroughly tapped into such extensive supervision signals. In this paper, we introduce the Med-ST framework for fine-grained spatial and temporal modeling to exploit information from multiple spatial views of chest radiographs and temporal historical records. For spatial modeling, Med-ST employs the Mixture of View Expert (MoVE) architecture to integrate different visual features from both frontal and lateral views. To achieve a more comprehensive alignment, Med-ST not only establishes the global alignment between whole images and texts but also introduces modality-weighted local alignment between text tokens and spatial regions of images. For temporal modeling, we propose a novel cross-modal bidirectional cycle consistency objective by forward mapping classification (FMC) and reverse mapping regression (RMR). By perceiving temporal information from simple to complex, Med-ST can learn temporal semantics. Experimental results across four distinct tasks demonstrate the effectiveness of Med-ST, especially in temporal classification tasks. Our code and model are available at https://github.com/SVT-Yang/MedST.

The goal of automatic report generation is to generate a clinically accurate and coherent phrase from a single given X-ray image, which could alleviate the workload of traditional radiology reporting. However, in a real-world scenario, radiologists frequently face the challenge of producing extensive reports derived from numerous medical images, thereby medical report generation from multi-image perspective is needed. In this paper, we propose the Complex Organ Mask Guided (termed as COMG) report generation model, which incorporates masks from multiple organs (e.g., bones, lungs, heart, and mediastinum), to provide more detailed information and guide the model's attention to these crucial body regions. Specifically, we leverage prior knowledge of the disease corresponding to each organ in the fusion process to enhance the disease identification phase during the report generation process. Additionally, cosine similarity loss is introduced as target function to ensure the convergence of cross-modal consistency and facilitate model optimization.Experimental results on two public datasets show that COMG achieves a 11.4% and 9.7% improvement in terms of BLEU@4 scores over the SOTA model KiUT on IU-Xray and MIMIC, respectively. The code is publicly available at https://github.com/GaryGuTC/COMG_model.

Purpose: Interpreting chest radiographs (CXR) remains challenging due to the ambiguity of overlapping structures such as the lungs, heart, and bones. To address this issue, we propose a novel method for extracting fine-grained anatomical structures in CXR using pseudo-labeling of three-dimensional computed tomography (CT) scans. Methods: We created a large-scale dataset of 10,021 thoracic CTs with 157 labels and applied an ensemble of 3D anatomy segmentation models to extract anatomical pseudo-labels. These labels were projected onto a two-dimensional plane, similar to the CXR, allowing the training of detailed semantic segmentation models for CXR without any manual annotation effort. Our anatomical segmentations allowed for the accurate extraction of relevant explainable medical features such as the cardio-thoracic-ratio. Conclusion: Our method of volumetric pseudo-labeling paired with CT projection offers a promising approach for detailed anatomical segmentation of CXR with a high agreement with human annotators. This technique may have important clinical implications, particularly in the analysis of various thoracic pathologies. Chest radiographs (CXR) are one of the most common diagnostic imaging methods for patients with respiratory or cardiovascular conditions, with more than 130 million studies performed annually in Germany alone [1]. By using ionizing radiation to penetrate the body, CXR provide a visual representation of the organs, tissues and cavities and their current state. The interpretation of CXR these images is challenging, since it requires a thorough understanding of human anatomy due to the presence of overlapping structures that can obscure potential pathological changes and other abnormalities. Despite these challenges, CXR remain a standard diagnostic procedure and its quantitative analysis can be time consuming. With the increasing demand in imaging procedures and the massive workload that comes along with it, this can lead to avoidable errors due to rushed examination [2, 3] or burnout due to the overly straining of doctors [4-6]. Recent advances in Computer Vision, such as convolutional neural networks (CNN) or vision transformers (ViT), have the potential to reduce the workload of radiologists in both image analysis and reporting [7, 8].

Knee pain is undoubtedly the most common musculoskeletal symptom that impairs quality of life, confines mobility and functionality across all ages. Knee pain is clinically evaluated by routine radiographs, where the widespread adoption of radiographic images and their availability at low cost, make them the principle component in the assessment of knee pain and knee pathologies, such as arthritis, trauma, and sport injuries. However, interpretation of the knee radiographs is still highly subjective, and overlapping structures within the radiographs and the large volume of images needing to be analyzed on a daily basis, make interpretation challenging for both naive and experienced practitioners. There is thus a need to implement an artificial intelligence strategy to objectively and automatically interpret knee radiographs, facilitating triage of abnormal radiographs in a timely fashion. The current work proposes an accurate and effective pipeline for autonomous detection, localization, and classification of knee joint area in plain radiographs combining the You Only Look Once (YOLO v3) deep convolutional neural network with a large and fully-annotated knee radiographs dataset. The present work is expected to stimulate more interest from the deep learning computer vision community to this pragmatic and clinical application.

Most deep learning models in chest X-ray prediction utilize the posteroanterior (PA) view due to the lack of other views available. PadChest is a large-scale chest X-ray dataset that has almost 200 labels and multiple views available. In this work, we use PadChest to explore multiple approaches to merging the PA and lateral views for predicting the radiological labels associated with the X-ray image. We find that different methods of merging the model utilize the lateral view differently. We also find that including the lateral view increases performance for 32 labels in the dataset, while being neutral for the others. The increase in overall performance is comparable to the one obtained by using only the PA view with twice the amount of patients in the training set.

Overall, the model detected and correctly localized 91 percent (310/340 images) and 96 percent (236/245 images) of all radius and ulna fractures on the frontal and lateral views, respectively. On a per-image basis the CNN achieved a sensitivity, specificity and AUC of 96 percent, 83 percent and 0.92, respectively for the frontal view. For the lateral views those numbers were 97 percent, 86 percent and 0.93, respectively. The per-study sensitivity, specificity and AUC were 98 percent, 73 percent and 0.89, respectively.