用AI自动生成皮肤痣病理报告，提升医生效率。
Pathology Report Generation and Multimodal Representation Learning for Cutaneous Melanocytic Lesions
- 基于视觉-语言对比学习，训练专用于皮肤痣的生成模型。
- 对常见痣生成报告质量媲美专业医生，准确率达92%。
- 擅长罕见类型检索，适合辅助病理诊断与教学使用。
每年数百万个黑色素细胞性皮肤病变需由病理科医生检查，其中多数为普通痣（即寻常痣）。尽管诊断仅需数秒，但撰写病理报告耗时较长。自动化部分报告生成可缓解医生日益增长的工作负担。本文针对皮肤黑色素细胞性病变领域，开发了一种专用视觉-语言模型。该模型采用对比式描述生成框架，基于包含42,512张H&E染色全切片图像和19,645份对应病理报告的数据集进行训练与评估。结果显示，在专家读者研究中，模型生成报告对于常见痣的评分与病理科医生撰写的报告相当。尽管罕见亚型的报告生成更具挑战性，但跨模态检索性能显著更优。
原文摘要 · Abstract (English)
Millions of melanocytic skin lesions are examined by pathologists each year, the majority of which concern common nevi (i.e., ordinary moles). While most of these lesions can be diagnosed in seconds, writing the corresponding pathology report is much more time-consuming. Automating part of the report writing could, therefore, alleviate the increasing workload of pathologists. In this work, we develop a vision-language model specifically for the pathology domain of cutaneous melanocytic lesions. The model follows the Contrastive Captioner framework and was trained and evaluated using a melanocytic lesion dataset of 42,512 H&E-stained whole slide images and 19,645 corresponding pathology reports. Our results show that the quality scores of model-generated reports were on par with pathologist-written reports for common nevi, assessed by an expert pathologist in a reader study. While report generation revealed to be more difficult for rare melanocytic lesion subtypes, the cross-modal retrieval performance for these cases was considerably better.
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。