Integrating Medical Imaging and Clinical Reports Using Multimodal Deep Learning for Advanced Disease Analysis

Medical image reporting is an important information carrier for medical staff to record medical activities for patients. It contains a large number of technical terms and medical knowledge. Extracting effective information from medical imaging reports can better serve clinical decision making and promote the development of the medical field. This paper focuses on the breast medical imaging report, analyzes the structural characteristics of the report, designs the medical record structured template, extracts the text features from the image report, and forms the structured data in the canonical form. In this paper, the machine learning model bidirectional CNNs-LSTM-CRF is used to extract the characteristic information of related lesions in the image report. The experimental data comes from an imaging inspection report provided by a medical institution to evaluate the effect of the structure by predicting the BI-RADS classification information. The data was provided by a medical institution, and the extracting result of the feature tagging is that the average accuracy is 95.71%, the average recall is 98.10%, and the average F1 values 97.29%.

Extracting information from the medical imaging reports with domain ontology has attracted much attention in medical natural language processing field. Based on the unstructured characteristics of medical image report text, the existing image report domain ontology is constructed by ontology learning method, including ontology learning method based on language and ontology learning method based on machine learning. However, these existing methods ignore the anatomical knowledge embedded in medical imaging reports, which is very useful for extracting entity relationships from reports. In order to solve the above problems, this paper proposes a domain ontology construction method for medical image reporting based on anatomy knowledge, which combines the prior knowledge of pathology and anatomy to obtain the basic framework of the domain ontology as the knowledge driver. In particular, our proposed approach consists of two tasks. The first task is to convert each text report into a semantic tree through an anatomic knowledge based semantic subtree generation algorithm. Semantic subtree generation algorithm mainly consists of three parts: framework positioning, relation extraction and adding relation. Firstly, the text is located based on anatomical knowledge, and the relational extraction mainly adopts the method of dependency syntactic analysis to extract the three semantic relations contained in the text. Finally, add the relationship to the corresponding branch. The second task is to obtain the domain ontology by merging semantic subtree. This step is mainly to obtain the domain ontology by merging the nodes of the XML structure semantic tree. The experimental results show that this method can be used for relational extraction and domain ontology construction, laying a good foundation for the follow-up research.

The research progress in multimodal learning has grown rapidly over the last decade in several areas, especially in computer vision. The growing potential of multimodal data streams and deep learning algorithms has contributed to the increasing universality of deep multimodal learning. This involves the development of models capable of processing and analyzing the multimodal information uniformly. Unstructured real-world data can inherently take many forms, also known as modalities, often including visual and textual content. Extracting relevant patterns from this kind of data is still a motivating goal for researchers in deep learning. In this paper, we seek to improve the understanding of key concepts and algorithms of deep multimodal learning for the computer vision community by exploring how to generate deep models that consider the integration and combination of heterogeneous visual cues across sensory modalities. In particular, we summarize six perspectives from the current literature on deep multimodal learning, namely: multimodal data representation, multimodal fusion (i.e., both traditional and deep learning-based schemes), multitask learning, multimodal alignment, multimodal transfer learning, and zero-shot learning. We also survey current multimodal applications and present a collection of benchmark datasets for solving problems in various vision domains. Finally, we highlight the limitations and challenges of deep multimodal learning and provide insights and directions for future research.

This paper presents an approach for adaptive scheduling and robustness optimization in global logistics networks by integrating multimodal deep reinforcement learning with Internet of Things (IoT) technologies. We propose an integrated framework comprising a multimodal data fusion mechanism that synthesizes heterogeneous IoT sensor data, historical records, and contextual information; an adaptive deep reinforcement learning architecture that generates dynamic scheduling policies; and a multi-objective robust optimization method that balances operational efficiency with system resilience. The framework addresses key challenges in global logistics including demand volatility, transportation disruptions, and environmental uncertainties. Comprehensive experiments conducted on real-world logistics datasets demonstrate that our approach outperforms traditional methods with an 18.7% reduction in operational costs, 12.4% improvement in service levels, and significantly enhanced robustness under various disruption scenarios. The proposed method maintains 83% performance stability during complex disruptions compared to 51–72% for alternative approaches, while keeping computational requirements feasible for practical deployment. This research demonstrates potential contributions to AI-driven logistics operations management by showing improved supply chain performance through multimodal learning and robust optimization techniques.

Rapid collection of data sources, varying in volume and structure poses a challenge for scientists to establish a practical approach to manipulating heterogeneous data sources. A multimodal learning and an integrated analysis make it possible to extract much worthwhile information from a collection of multiple simple raw data. Therefore, data integration can lead to a more reliable and robust result. High-throughput sequencing technologies, especially next-generation sequencing, leave us with multi-platform genomic data such as gene expression, SNP, CNV, DNA methylation, and miRNA expression. In this paper, we represented a multimodal deep neural network to exploit the mutual information between three different modalities to classify breast cancer patients into two groups based on their survival rate. Experimental results indicate that our method improves the classification accuracy and performs better on imbalanced data compared to the other single-modal state-of-the-art methods.

This article proposes a novel multitask learning framework that integrates contrastive learning and natural language generation (NLG) to enhance medical image classification and report generation. The goal is to improve disease classification accuracy and interpretability in medical diagnostics. The model architecture consists of a Vision Transformer (ViT) as a visual encoder, a transformer-based text encoder, and a multimodal decoder. The visual encoder processes medical images, while the text encoder handles disease-related text prompts. These components are trained jointly using image-text contrastive loss and language generation loss. Evaluations on the MIMICCXR and Chexpert datasets show that the model with NLG (Plain + NLG) outperforms the baseline contrastive learning model (Plain) in disease classification. For example, in the MIMICCXR dataset, the accuracy for Atelectasis increased from 17.44%(Plain) to 41.5% (Plain + NLG), and for Cardiomegaly, it improved from 19.25% to 47.4%. In Chexpert, the accuracy for Atelectasis increased from 12.5% to 58.5%, and for Pleural Effusion, from 61.10% to 64.0%. The model also demonstrated improvements in F1 scores, particularly for complex diseases like Cardiomegaly and Consolidation. The proposed multitask framework effectively combines contrastive learning with NLG, leading to improved disease classification and medical report generation. This approach has potential clinical applications by enhancing AI’s interpretability and accuracy in medical decision-making.

In recent years, deep learning technology has made breakthroughs in computer vision. After using large-scale data training, the deep neural network represented by GAN is significantly better than previous technologies in image generation, including generating more reasonable, higher-definition, more complex, and more accurate images. With the continuous development of datasets, models, and applications, the fusion of different modal information, including fusion of natural language, semantic layouts, tags, edge maps, and other different modal information, to generate images has become a new demand and challenge. There are related reviews on image generation and multimodal deep learning. However, there has not been a review dedicated to multimodal deep-learning image generation to discuss the current status, existing problems, and challenges of this task. Therefore, this review proposes a survey on multimodal deep learning image generation. It aims to provide readers with an application scenario for multimodal deep learning image generation. Also, it provides readers with new multimodal deep learning image generation technologies, the relevant datasets, evaluation metrics used, and some results comparison. Finally, this article describes some of the challenges and future topics of multimodal deep learning image generation.

IntroductionIncorporating artificial intelligence (AI) in diagnostic medical imaging reports has the potential to improve efficiency. Although perception of radiologists, radiographers, medical students and patients on AI use in image reporting has been explored, there is limited literature on non‐radiologist clinicians' opinion on this topic.MethodSingle‐centre online survey targeting non‐radiologist medical staff conducted from May to August 2021 at a tertiary referral hospital in Melbourne, Australia. Survey questions revolved around clinicians' level of comfort acting on AI‐generated reports with varying levels of radiologist involvement and scan complexity, opinion on medicolegal responsibility for erroneous AI‐issued reports and perception of data privacy and security.ResultsEighty‐eight responses were collected, including 47.9% of consultants. Non‐radiologist clinicians across all seniorities and specialties felt significantly less comfortable acting on AI‐issued reports compared with radiologist‐issued reports (mean comfort radiologist 6.44/7, mean comfort AI 3.35/7, P < 0.001) but felt equally comfortable with an AI‐hybrid model of care (mean comfort hybrid 6.38/7, P = 0.676). Non‐radiologist clinicians believed that medicolegal responsibility with errors in AI‐issued reports mostly lay with hospitals or health service providers (65.9%) and radiologists (54.5%). Regarding data privacy and security, non‐radiologist clinicians felt significantly less comfortable with AI issuing image reports instead of radiologists (P < 0.001).ConclusionA hybrid AI‐generated radiologist‐confirmed method of image reporting may be the ideal way of integrating AI into clinical practice based on the perception of our referring non‐radiologist medical colleagues. Formal guidelines on medicolegal responsibility and data privacy should be established prior to utilising AI in the clinical setting.

In September 2006, the Royal Australian and New Zealand College of Radiologists (RANZCR) endorsed the modified Peer Review Audit Tool (PRAT). We aimed to assess the feasibility of using this tool in a busy radiation oncology department using an electronic medical record (EMR) system, identify areas of compliance and assess the impact of the audit process on patient management. Fortnightly random clinical audit was undertaken by using the revised RANZCR PRAT in the departments of radiation oncology at Liverpool and Macarthur Cancer Therapy Centres (LCTC and MCTC). Following audit of the EMR, treatment plans were audited by peer review. Data were collected prospectively from June 2007 to June 2008. Audits were carried out on 208 patients. Behaviour criteria were well documented in the EMR, but scanning of histology and medical imaging reports did not occur in up to a third of cases. With electronic prescriptions, treatment prescription errors were rare. In total, 8 (3.8%) out of 208 patients had a change to management recommended. Variability in interpretation of PRAT 'protocol/study' criteria was identified. We found that real-time audit is feasible and effective in detecting both issues with documentation in the EMR, and a small number of patients in whom a change to management is recommended. Recommendations have been made in order to continue to improve the audit process including documentation of any changes recommended and whether the recommended change occurred.

The automatic description of images has made good advances, but limited by the little-sample dataset, that the automatic generation of medical imaging reports is still a challenging problem. Aiming at the problem of training the joint model (CNN-RNN) on little-sample datasets, this paper proposes an improved encoding-decoding mode, in which the encoder uses less parameter in FCN (Fully Convolutional Network) for identifying lesions in mammography, and encoding it into a semantic vector. The decoder uses a LSTM (Long Short-Term Memory network) for solving, thereby reducing sample requirements. In addition, this paper combines multi-label classification (MLC) to assist the semantic coding process and uses post-processing such as the beam search to make the output fit in the natural language description better. Compared to existing models, our improved model on public mammography dataset (INbreast) with real-world data supplement achieved the BLEU score improvements by two points.

Rethinking masked image modelling for medical image representation

A method for extracting tumor events from clinical CT examination reports

Nowadays, the increasing number of medical diagnostic data and clinical data provide more complementary references for doctors to make diagnosis to patients. For example, with medical data, such as electrocardiography (ECG), machine learning algorithms can be used to identify and diagnose heart disease to reduce the workload of doctors. However, ECG data is always exposed to various kinds of noise and interference in reality, and medical diagnostics only based on one-dimensional ECG data is not trustable enough. By extracting new features from other types of medical data, we can implement enhanced recognition methods, called multimodal learning. Multimodal learning helps models to process data from a range of different sources, eliminate the requirement for training each single learning modality, and improve the robustness of models with the diversity of data. Growing number of articles in recent years have been devoted to investigating how to extract data from different sources and build accurate multimodal machine learning models, or deep learning models for medical diagnostics. This paper reviews and summarizes several recent papers that dealing with multimodal machine learning in disease detection, and identify topics for future research.

Recent advances in deep learning and natural language processing (NLP) have broadened opportunities for automatic text processing in the medical field. However, the development of models for low-resource languages like French is challenged by limited datasets, often due to legal restrictions. Large-scale training of medical imaging models often requires extracting labels from radiology text reports. Current methods for report labeling primarily rely on sophisticated feature engineering based on medical domain knowledge or manual annotations by radiologists. These methods can be labor-intensive. In this work, we introduce a BERT-based approach for the efficient labeling of French mammogram image reports. Our method leverages both the expansive scale of existing rule-based systems and the precision of radiologist annotations. Our experimental results showcase the superiority of the proposed approach. It was initially fine-tuned on a limited dataset of radiologist annotations. Then, it underwent training on annotations generated by a rule-based labeler. Our findings reveal that our final model, MammoBERT, significantly outperforms the rule-based labeler while simultaneously reducing the necessity for radiologist annotations during training. This research not only advances the state of the art in medical image report labeling but also offers an efficient and effective solution for large-scale medical imaging model development.

Preoperative Prediction of Axillary Lymph Node Metastasis in Patients With Breast Cancer Through Multimodal Deep Learning Based on Ultrasound and Magnetic Resonance Imaging Images