Multimodal medical image segmentation is always a critical problem in medical image segmentation. Traditional deep learning methods utilize fully CNNs for encoding given images, thus leading to deficiency of long-range dependencies and bad generalization performance. Recently, a sequence of Transformer-based methodologies emerges in the field of image processing, which brings great generalization and performance in various tasks. On the other hand, traditional CNNs have their own advantages, such as rapid convergence and local representations. Therefore, we analyze a hybrid multimodal segmentation method based on Transformers and CNNs and propose a novel architecture, HybridCTrm network. We conduct experiments using HybridCTrm on two benchmark datasets and compare with HyperDenseNet, a network based on fully CNNs. Results show that our HybridCTrm outperforms HyperDenseNet on most of the evaluation metrics. Furthermore, we analyze the influence of the depth of Transformer on the performance. Besides, we visualize the results and carefully explore how our hybrid methods improve on segmentations.

Machine Learning (ML) is an algorithm based on big data, which learns patterns from the previously observed data through classifying, predicting, and optimizing to accomplish specific tasks. In recent years, there has been rapid development in the field of ML in medicine, including lung imaging analysis, intensive medical monitoring, mechanical ventilation, and there is need for intubation etiology prediction evaluation, pulmonary function evaluation and prediction, obstructive sleep apnea, such as biological information monitoring and so on. ML can have good performance and is a great potential tool, especially in the imaging diagnosis of interstitial lung disease. Idiopathic pulmonary fibrosis (IPF) is a major problem in the treatment of respiratory diseases, due to the abnormal proliferation of fibroblasts, leading to lung tissue destruction. The diagnosis mainly depends on the early detection of imaging and early treatment, which can effectively prolong the life of patients. If the computer can be used to assist the examination results related to the effects of fibrosis, a timely diagnosis of such diseases will be of great value to both doctors and patients. We also previously proposed a machine learning algorithm model that can play a good clinical guiding role in early imaging prediction of idiopathic pulmonary fibrosis. At present, AI and machine learning have great potential and ability to transform many aspects of respiratory medicine and are the focus and hotspot of research. AI needs to become an invisible, seamless, and impartial auxiliary tool to help patients and doctors make better decisions in an efficient, effective, and acceptable way. The purpose of this paper is to review the current application of machine learning in various aspects of respiratory diseases, with the hope to provide some help and guidance for clinicians when applying algorithm models.

Consumer electronics, together with artificial intelligence technology, are being widely applied in the medical field, aiding medical data collecting, health monitoring, disease diagnosis, and survival prediction. Training an effective and robust medical artificial intelligence model for cancer survival prediction deployed in consumer electronic devices requires a large amount of high-quality annotated data. However, due to limitations in computing resources and energy, it is impractical to train artificial intelligence models on large-scale medical data directly in the cloud. To address the above problem, we propose a cloud-edge collaboration framework to develop medical electronic devices for cancer survival prediction. The framework trains local models on edge medical electronic devices and aggregates the model weights on the cloud server. Furthermore, when transmitting model weights between cloud and edge, we introduce differential privacy technology to ensure security concerns. We evaluate our proposed cloud-edge collaboration framework on two cancer datasets from The Cancer Genome Atlas (TCGA). Experimental results demonstrate that our cloud-edge collaboration framework is highly effective in both survival prediction performance and patient privacy preservation, with the potential to develop medical consumer electronics.

The pathophysiology and clinical manifestations of pulmonary embolism are complex, heterogeneous, and the disease burden is severe, and its prediction and diagnosis are of major challenges. Artificial intelligence (AI) is a field of computer science that involves the development of programs and complex data analysis designed to replicate human cognitive processes. In recent years, with the continuous development of medical information technology, the application of AI in the diagnosis and treatment of diseases has made rapid progress, especially in the field of pulmonary embolism, which is mainly based on imaging. In this review, we summarize the current application prospects and directions of AI in early prediction, screening, diagnosis, and prognosis of PE, and discuss the main challenges and future of AI in pulmonary embolism (PE), in order to provide a theoretical basis for the application of AI in the risk assessment and standardized management of PE.

Multi-view feature learning can provide basic information for consistent grouping and is very common in practical applications, such as judicial document clustering. However, it is a challenge to combine multiple heterogeneous features to learn a comprehensive description of data samples. To solve this problem, many methods explore the correlation between various features across views by assuming that all views share the same semantic information. Inspired by this, in this paper we propose a new multi-view robust feature learning (MRFL) method. In addition to projecting features from different views to a shared semantic subspace, our approach also learns the irrelevant information of data space to capture the feature dependencies between views in potential common subspaces. Therefore, the MRFL can obtain flexible feature associations hidden in multi-view data. A new objective function is designed to derive and solve the effective optimization process of MRFL. Experiments on real-world multi-view datasets show that the proposed MRFL method is superior to the state-of-the-art multi-view learning methods.

Chronic obstructive pulmonary disease (COPD), characterized by high incidence and mortality rates, is a chronic respiratory disorder that places a substantial burden on healthcare systems. Artificial Intelligence (AI), with its deep integration into the medical field, particularly through its core branches—Machine Learning (ML) and Deep Learning (DL)—has demonstrated significant potential in the intervention and management of COPD. From early risk prediction based on multimodal data to the enhancement of precise diagnosis and treatment through radiomics and clinical decision support systems, and further to the dynamic assessment of acute exacerbation and comorbidity risks via machine learning models, AI has, in combination with bioinformatics and multi-omics analysis, established a novel intelligent management framework that spans the entire disease continuum. This framework offers innovative, individualized solutions aimed at alleviating the burden on healthcare systems. This article reviews the technical applications and clinical value of AI in the diagnosis, prevention, treatment, and prognosis of COPD, discusses current challenges, and outlines future development directions to provide insights for clinical practice and research.