数字病理学中组织图像搜索与检索的验证
On Validation of Search & Retrieval of Tissue Images in Digital Pathology
AI总结：
本文讨论数字病理组织图像搜索与检索的验证，强调 CBIR 系统需从准确率、索引、搜索时间和存储开销等方面进行全面评估。
摘要：
医学图像通过为诊断、治疗规划和疾病监测提供关键信息，在现代医疗保健中发挥着至关重要的作用。放射学和病理学等领域高度依赖准确的图像判读：放射科医生检查 X 射线、CT 扫描和 MRI，以诊断从骨折到癌症的各类病症；病理科医生则利用显微镜和数字图像检测细胞异常，从而诊断癌症和感染。技术进步使医学图像的数量和复杂性呈指数级增长，因此需要高效的管理和检索工具。基于内容的图像检索（Content-Based Image Retrieval，CBIR）系统通过依据视觉内容搜索和检索图像来满足这一需求，使临床医生能够查找相似病例并比较病理模式，从而提高诊断准确性。对医学应用中的图像搜索引擎进行全面验证，涉及评估准确率、索引和搜索时间以及存储开销等性能指标，以确保可靠、高效地检索准确结果；近期组织病理学领域的验证工作已证明了这一点。
英文摘要：
Medical images play a crucial role in modern healthcare by providing vital information for diagnosis, treatment planning, and disease monitoring. Fields such as radiology and pathology rely heavily on accurate image interpretation, with radiologists examining X-rays, CT scans, and MRIs to diagnose conditions from fractures to cancer, while pathologists use microscopy and digital images to detect cellular abnormalities for diagnosing cancers and infections. The technological advancements have exponentially increased the volume and complexity of medical images, necessitating efficient tools for management and retrieval. Content-Based Image Retrieval (CBIR) systems address this need by searching and retrieving images based on visual content, enhancing diagnostic accuracy by allowing clinicians to find similar cases and compare pathological patterns. Comprehensive validation of image search engines in medical applications involves evaluating performance metrics like accuracy, indexing, and search times, and storage overhead, ensuring reliable and efficient retrieval of accurate results, as demonstrated by recent validations in histopathology.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
