深度学习让医学影像分析更准更快，自动识别病灶。
Deep Learning Applications in Medical Image Analysis: Advancements, Challenges, and Future Directions
- 用卷积神经网络自动提取医学影像特征，省去人工标注。
- 在病理、放射、眼科等领域实现疾病检测与分割的高精度。
- 适合医疗AI研究者和临床医生参考，推动智能诊断发展。
医学图像分析已成为现代医疗的关键环节，有助于医生实现快速精准诊断。深度学习作为人工智能的重要分支，显著推动了医学影像分析的发展，提升了临床流程的准确性和效率。卷积神经网络（CNN）等深度学习算法能够从多维医学影像（如MRI、CT、X-ray）中自主学习特征，无需人工特征提取。这些模型已广泛应用于病理学、放射学、眼科学和心脏病学等多个领域，支持疾病检测、分类与分割任务，在多项临床场景中表现出优异性能，为智慧医疗提供了有力支撑。
原文摘要 · Abstract (English)
Medical image analysis has emerged as an essential element of contemporary healthcare, facilitating physicians in achieving expedited and precise diagnosis. Recent breakthroughs in deep learning, a subset of artificial intelligence, have markedly revolutionized the analysis of medical pictures, improving the accuracy and efficiency of clinical procedures. Deep learning algorithms, especially convolutional neural networks (CNNs), have demonstrated remarkable proficiency in autonomously learning features from multidimensional medical pictures, including MRI, CT, and X-ray scans, without the necessity for manual feature extraction. These models have been utilized across multiple medical disciplines, including pathology, radiology, ophthalmology, and cardiology, where they aid in illness detection, classification, and segmentation tasks......
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。