癌症诊断的新方法:整合全息显微医学成像和深度学习技术-挑战和未来趋势。

IF 1.3 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING Biomedical Physics & Engineering Express Pub Date : 2024-12-13 DOI:10.1088/2057-1976/ad9eb7
Asifa Nazir, Ahsan Hussain, Mandeep Singh, Assif Assad
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引用次数: 0

摘要

医学成像在早期疾病诊断中至关重要,它提供了能够及时准确检测健康异常的基本见解。传统的成像技术,如磁共振成像(MRI)、计算机断层扫描(CT)、超声波和正电子发射断层扫描(PET),提供了对三维结构的重要见解,但往往无法提供全面和详细的解剖分析,只能捕获振幅细节。三维全息显微医学成像通过捕获生物结构的振幅(亮度)和相位(结构信息)细节提供了一个有前途的解决方案。在这项研究中,我们探讨了深度学习(DL)和全息显微相位成像在癌症诊断中的新型协作潜力。本研究通过综合定量相位成像(QPI)和DL方法对现有文献进行了全面的研究,分析了研究进展,确定了研究差距,并提出了癌症诊断的未来研究方向。这种新方法通过捕获详细的结构信息,解决了传统成像的一个关键限制,为更准确的诊断铺平了道路。提出的方法包括组织样本收集、全息图像扫描、不平衡数据集的预处理,以及使用U-Net和Vision Transformer(ViT)等DL架构对带注释的数据集进行训练。此外,DL中的复杂概念,如可解释人工智能技术(XAI)的结合,被建议用于全面的疾病诊断和识别。本研究深入探讨了全息成像与深度影像相结合在肿瘤精确诊断中的优势。此外,通过识别与此集成方法相关的挑战,提供了细致的见解。
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A Novel Approach in Cancer Diagnosis: Integrating Holography Microscopic Medical Imaging and Deep Learning Techniques - Challenges and Future Trends.

Medical imaging is pivotal in early disease diagnosis, providing essential insights that enable timely and accurate detection of health anomalies. Traditional imaging techniques, such as Magnetic Resonance Imaging (MRI), Computer Tomography (CT), ultrasound, and Positron Emission Tomography (PET), offer vital insights into three-dimensional structures but frequently fall short of delivering a comprehensive and detailed anatomical analysis, capturing only amplitude details. Three-dimensional holography microscopic medical imaging provides a promising solution by capturing the amplitude (brightness) and phase (structural information) details of biological structures. In this study, we investigate the novel collaborative potential of Deep Learning (DL) and holography microscopic phase imaging for cancer diagnosis. The study comprehensively examines existing literature, analyzes advancements, identifies research gaps, and proposes future research directions in cancer diagnosis through the integrated Quantitative Phase Imaging (QPI) and DL methodology. This novel approach addresses a critical limitation of traditional imaging by capturing detailed structural information, paving the way for more accurate diagnostics. The proposed approach comprises tissue sample collection, holographic image scanning, pre-processing in case of imbalanced datasets, and training on annotated datasets using DL architectures like U-Net and Vision Transformer(ViT's). Furthermore, sophisticated concepts in DL, like the incorporation of Explainable AI techniques (XAI), are suggested for comprehensive disease diagnosis and identification. The study thoroughly investigates the advantages of integrating holography imaging and DL for precise cancer diagnosis. Additionally, meticulous insights are presented by identifying the challenges associated with this integration methodology.

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来源期刊
Biomedical Physics & Engineering Express
Biomedical Physics & Engineering Express RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING-
CiteScore
2.80
自引率
0.00%
发文量
153
期刊介绍: BPEX is an inclusive, international, multidisciplinary journal devoted to publishing new research on any application of physics and/or engineering in medicine and/or biology. Characterized by a broad geographical coverage and a fast-track peer-review process, relevant topics include all aspects of biophysics, medical physics and biomedical engineering. Papers that are almost entirely clinical or biological in their focus are not suitable. The journal has an emphasis on publishing interdisciplinary work and bringing research fields together, encompassing experimental, theoretical and computational work.
期刊最新文献
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