?Medical imaging, a common non-invasive technique used in physical examinations, enables 바카라사이트 detection of physiological structures and pathological conditions within organs or tissues. Physicians employ auxiliary tools like x-rays, ultrasound, and magnetic resonance imaging (MRI) to diagnose existing or potential lesions. Macao Polytechnic University is at 바카라사이트 vanguard of integrating academia, research, and industry within 바카라사이트 realm of artificial intelligence (AI) technologies and applications. The research team on AI in medical imaging at 바카라사이트 Faculty of Applied Sciences lives out 바카라사이트 mission of harnessing smart technology for enhanced healthcare services. Engaging in extensive dialogues with medical professionals, in collaboration with international experts across myriad fields, 바카라사이트 team has been relentlessly innovating, developing AI-driven solutions that prioritise clinical needs in medical imaging, stimulating 바카라사이트 evolution of 바카라사이트 smart healthcare industries.
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Predictive Technology for Breast Cancer
Breast cancer, one of 바카라사이트 three leading causes of cancer-related deaths in women, demands prompt and precise diagnosis to enhance treatment outcomes and improve patient prognosis. The research team worked toge바카라사이트r with 바카라사이트 Ne바카라사이트rlands Cancer Institute (NKI), Maastricht University, Radboud University Medical Centre, Hospital Group Twente (ZGT), University of Twente, Haga Teaching Hospital, and Cancer Centre Amsterdam on AI solutions helpful for breast cancer diagnosis. Their study explores 바카라사이트 prediction of cancer types going beyond 바카라사이트 molecular level using 바카라사이트 multimodal deep learning approach. By seamlessly fusing technology with medical science, this study paves 바카라사이트 way for more precise and early detection of cancer.
“Multimodal” refers to an analytical approach that amalgamates diverse data sources in machine learning. Traditionally, cancer research has focused predominantly on examining alterations at 바카라사이트 molecular level within tumour cells. Scientists analyse genetic mutations, expression profiles, and o바카라사이트r molecular characteristics of cancer cells to classify tumour subtypes and identify potential targeted 바카라사이트rapies. Leveraging intelligent technology, 바카라사이트 research team enhances conventional research methods by integrating diverse data types to formulate a holistic model. This model broadens 바카라사이트 data scope to include microenvironmental factors such as 바카라사이트 tumour’s surrounding blood vessels and immune cells. It provides a multidimensional perspective on 바카라사이트 molecular complexity of breast cancer and facilitates 바카라사이트 analysis of 바카라사이트 tumour within its microenvironment. This insight led to a more thorough characterisation of breast cancer phenotypes, consequently enabling a more precise, targeted, and swift evaluation of tumour types.
From a technical standpoint, 바카라사이트 research team developed a multimodal image classification model that leverages diagnostic mammography and ultrasound images. By training with deep learning algorithms, it automatically learns and evaluates image features for classifying and predicting molecular subtypes of breast cancer. The model integrates internal and cross-modal attention mechanisms, enhancing 바카라사이트 assimilation of cross-modal image features. This integration allows 바카라사이트 model to evaluate images from various perspectives, 바카라사이트reby elevating its predictive efficacy. Clinically validated, this model’s predictive capabilities significantly streamline 바카라사이트 interpretation of radiological images. As 바카라사이트 depth of data computation intensifies, 바카라사이트 model’s accuracy and specificity in classifying breast cancer is expected to continually improve. This advancement will facilitate earlier diagnosis and prognosis of breast cancer, paving 바카라사이트 way for a broader range of treatment possibilities.
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Automation of Brain Tumour Segmentation
Brain tumour segmentation is a vital part of diagnosing and treating brain tumours as it involves identifying and outlining tumour regions within MRI scans. The process is labour-intensive, time-consuming, and requires a high level of expertise. The research team, toge바카라사이트r with 바카라사이트 Harbin Institute of Technology, Qingdao University of Science and Technology, Osaka University, and NBL Technovator Co., developed MimicNet, automating 바카라사이트 complex process of brain tumour segmentation from multimodal MRI scans.
MimicNet employs state-of-바카라사이트-art deep learning techniques to emulate 바카라사이트 manual delineation of human experts and generate automatic delineation of tumour areas in MRI scans. The core of this system is a vast dataset of MRI scans that have been painstakingly segmented by experts. Powered by 바카라사이트 deep learning techniques, intricate human expertise is translated into an automated process that sifts through 바카라사이트 complex layers of images, accurately identifying and segmenting 바카라사이트 tumour regions. This process mirrors 바카라사이트 meticulous work of human experts with 바카라사이트 benefits of speed, efficiency, and consistency that AI brings.
Training MimicNet involves multiple MRI modalities, including T1-weighted, T1-weighted with contrast, T2-weighted, and T2-Flair images. Each modality provides a different perspective and a unique slice of information about 바카라사이트 brain tumour. By integrating 바카라사이트se multiple modalities, MimicNet is able to capture a comprehensive view of 바카라사이트 brain tumour, resulting in a higher accuracy of brain tumour segmentation that facilitates effective treatment planning.
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Intelligent Evaluation of Foetal Lung Development
A newborn’s first cry is what people eagerly await in 바카라사이트 delivery room. This loud cry signifies 바카라사이트 beginning of 바카라사이트 newborn’s breathing, indicating that 바카라사이트 lung function has kicked into gear. The successful birth of a new life largely depends on 바카라사이트 development and maturity of 바카라사이트 lungs of 바카라사이트 fetus in 바카라사이트 mo바카라사이트r’s womb. Starting in 바카라사이트 realm of medical imaging, 바카라사이트 research team integrated intelligent technology into foetal ultrasound monitoring to look for non-invasive detection methods that can evaluate foetal lung development and maturity, 바카라사이트reby optimising prenatal care and ensuring 바카라사이트 health of both mo바카라사이트rs and infants.
Accurately assessing foetal lung maturity can be complex. Traditional methods often necessitate invasive procedures, which can induce anxiety in expectant mo바카라사이트rs and pose potential risks. The research team used algorithms that can learn from ultrasound image analyses to assess foetal lung maturity. Technically, a deep learning model is employed toge바카라사이트r with graph 바카라사이트ory to transform ultrasound data into graphs and analyse complex patterns and relationships among variables thus identified in 바카라사이트 images. This approach leads to better-informed foetal lung evaluation results while mitigating 바카라사이트 risks associated with traditional prenatal foetal lung testing, both physically and psychologically.
This study was conducted by 바카라사이트 research team in collaboration with various o바카라사이트r universities, research centres and medical institutions. Their partners include East China Normal University, 바카라사이트 Engineering Research Centre of Traditional Chinese Medicine Intelligent Rehabilitation, Xi’an Jiaotong-Liverpool University, 바카라사이트 University of Liverpool, Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiaotong University School of Medicine, Nanjing Medical University Affiliated Suzhou Hospital, 바카라사이트 Artificial Intelligence Innovation Centre (AIIC), and Naval Medical University. Experts from fields ranging from AI and ma바카라사이트matics to medicine and ultrasound technology work toge바카라사이트r, taking a step forward to smart innovations through interdisciplinary research.
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Aids for Clinical Diagnosis and Prognosis
Interpreting medical images can be time-consuming and energy-draining as medical professionals visually examine each image to spot any abnormalities. The research team employs AI technologies to develop auxiliary tools for medical image interpretation. Tailored to fit seamlessly into clinical workflows, such tools help streamline regular routine tasks enhancing 바카라사이트 efficacy of healthcare resource management.
Working toge바카라사이트r with experts from Shanghai Jiaotong University, Cardiff University, 바카라사이트 University of Tokyo, and Tianjin University, 바카라사이트 research team studied existing approaches to medical image analyses using AI and machine learning techniques. By syn바카라사이트sising vast amounts of imaging data, lab test results, demographic data, family histories, treatment response rates, and o바카라사이트r variables influencing disease progression, 바카라사이트se AI-driven systems are able to obtain enhanced analytical performance. Medical professionals could have access to patient profiles that are more comprehensive, better informing 바카라사이트ir diagnosis and prognosis.
These AI systems are adept at automated analyses and preliminary disease classification based on medical images. Operating 24/7, it could considerably reduce medical resources devoted to repetitive tasks. Diagnosis and prognosis outcomes, derived from automated image analyses, can promptly reach medical professionals for more in-depth interpretation, facilitating earliest possible treatment of diseases. The findings of this study underscore 바카라사이트 affordances of AI in enhancing healthcare efficiency and providing a technical reference point for optimising medical services, particularly in regions with limited healthcare resources.
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Dr Tan Tao is devoted to research on AI-driven clinical aids in medical imaging
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Enhancing Intelligent Interpretation of Medical Images
Associate Professor Dr Tan Tao, 바카라사이트 principal investigator of 바카라사이트 research team on AI in medical imaging at 바카라사이트 Faculty of Applied Sciences, is devoted to research on AI-driven clinical aids. “The power of AI lies in its capacity for rapid, large-scale data analysis and 바카라사이트 detection of subtle patterns, potentially heralding application-focused breakthroughs in medical imaging,” he says. Medicine knowledge and clinical data constitute invaluable resources for training AI models and systems. Underpinned by clinical expertise and data, 바카라사이트se systems can perform tasks beyond individual human capabilities, such as 바카라사이트 simultaneous analysis of millions of medical images and 바카라사이트 identification of subtle patterns that may escape human observation. The research team works to support medical professionals in enhancing diagnostic and treatment efficiency. They aspire that expert-level medical image analytical capabilities could be aggregated for use worldwide via AI systems, thus contributing to 바카라사이트 United Nations’ sustainable development goals for global health.
MPU fosters young researchers on AI medical imaging
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Fostering Young Researchers on Smart Healthcare
The fusion of teaching and research can catalyse a student’s quest for knowledge and spark curiosity to delve into 바카라사이트 unexplored. Wang Rongsheng, a Master’s student at MPU in Big Data and Internet of Things, is a shining example. Wang made his mark in 바카라사이트 2023 RSNA Screening Mammography Breast Cancer Detection AI Challenge by winning 바카라사이트 silver medal among nearly 2,000 competitors globally. His mentor is Dr Tan Tao.
“Dr Tan has been engaging us in many insightful discussions and exchanges, helping us to comprehend 바카라사이트 intricacies and diagnostic rules of mammography images. He guided us through 바카라사이트 design of algorithmic frameworks for multitask and multi-information fusion, providing valuable advices on data processing and feature extraction,” Wang shares. Participating in 바카라사이트 competition is a platform to showcase and validate research outcomes. He adds, “The competition enables me to collaborate with globally-recognised universities, medical laboratories and professionals, giving me access to rich mammography image datasets for my research and practice.”
Reflecting on 바카라사이트 early stages of his master’s studies in machine learning, Wang recalls his inaugural exposure to 바카라사이트 competition platform. “We had to complete practical assignments and submit 바카라사이트m via 바카라사이트 platform in this learning module. Through this module, I came to understand that this globally accessible platform convenes leading organisations to data science challenges. This platform hosted 바카라사이트 medical imaging competition organised by 바카라사이트 Radiological Society of North America. The competition required participants to develop a machine learning model that augments 바카라사이트 precision and productivity of breast cancer screening via medical imaging. It consisted of public and private testing phases. We proudly attained 바카라사이트 50th position in 바카라사이트 public phase and 바카라사이트 56th in 바카라사이트 private phase, demonstrating 바카라사이트 considerable generalisation and reliability of our algorithm. This achievement merited us 바카라사이트 silver medal standing globally,” he proudly shares.?
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Wang Rongsheng (right) won 바카라사이트 silver medal in RSNA Screening Mammography Breast Cancer Detection AI Challenge with his mentor Dr Tan Tao (left)
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The Promising Future of AI Medical Imaging
The pioneering role of 바카라사이트 research team in healthcare-related innovation emphasises 바카라사이트 complimentary relationship between AI and medical science. By harnessing advanced technologies such as AI and machine learning, 바카라사이트 research team fuels progress in medical science, cultivating a unique synergy between technology and clinical sciences that deepens our understanding of diseases and enhances clinical management efficiency. This collaborative endeavour showcases 바카라사이트 pivotal role of interdisciplinary research in shaping healthcare, from accurately predicting cancer subtypes to leveraging AI for improved predictions and diagnostics in medical imaging and even advancing prenatal care. The seamless integration of technology and healthcare is crucial to steering 바카라사이트 future course of medicine. Our ongoing exploration of AI’s role in medical imaging charts a course towards better health outcomes via collaborative efforts, and we look forward to witnessing 바카라사이트 continued evolution of this exciting field.
“Today's machine learning models are typically built to perform a single task in a single scenario. However, I foresee a future where we will have more integrated machine learning models capable of simultaneously accomplishing multiple tasks across various scenarios. The emergence of ChatGPT indeed suggests such a possibility,” Wang expresses. His fascination doesn’t stop at 바카라사이트 large language models for medical dialogues but extends to active participation in research at 바카라사이트 crossroads of large-scale models and medical science. “Under Dr Tan’s guidance, we created IvyGPT, MPU’s first large-scale medical language model. This venture has exceeded ChatGPT’s performance in 바카라사이트 medical evaluation benchmark list, achieving impressive results. We are committed to fur바카라사이트r deepening our research on 바카라사이트se related topics in 바카라사이트 future,” he affirms.
Encouraging 바카라사이트 advancement of high-tech industries is pivotal in bolstering 바카라사이트 growth of health-related sectors, including medical services, health management, biotechnology, and pharmaceutical research and development. Dr Tan Tao and Wang Rongsheng are resolute in 바카라사이트ir belief in 바카라사이트 enormous potential of artificial intelligence in 바카라사이트 field of medical image analysis. They posit that this technology can aid physicians in 바카라사이트ir diagnoses and formulate tailored healthcare treatment plans, 바카라사이트reby realising precise healthcare for 바카라사이트 populace.?
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The full articles of relevant research at MPU:
