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.

本研究解决了数字病理学中医疗图像检索的效率和准确性问题。通过验证内容为基础的图像检索（CBIR）系统，本研究提出了一种全面评估图像搜索引擎在医疗应用中的有效性的方法。最重要的发现是这些系统能够显著提升病例的诊断准确性，从而为医生提供更可靠的支持。 

数字病理学中组织图像的搜索与检索验证