用报告代替大量标注，让AI高效识别多种早期肿瘤。
Scaling Artificial Intelligence for Multi-Tumor Early Detection with More Reports, Fewer Masks
- 利用临床报告中的描述训练AI分割肿瘤，减少对人工标注的依赖。
- 仅用10万份报告训练的模型性能媲美用723个标注图训练的模型。
- 首次实现脾脏、胆囊等器官肿瘤的自动化分割，适用于无公开标注的数据。
早期肿瘤检测可挽救生命。全球每年超过3亿次计算机断层扫描（CT）检查，为癌症筛查提供了巨大机遇。然而，即使对专家而言，在这些CT图像中发现微小或早期肿瘤仍具挑战性。人工智能（AI）可通过标记可疑区域辅助诊断，但传统训练需大量由放射科医生手动绘制的肿瘤掩码——这既耗时又昂贵。相比之下，几乎所有临床CT都附有医学报告，包含肿瘤大小、数量、形态甚至病理结果等丰富信息，却未被充分用于AI训练。我们提出R-Super，使AI学习根据报告描述自动识别匹配的肿瘤。该方法借助海量现成报告进行大规模训练，显著降低对人工掩码的需求。在使用101,654份报告训练后，模型性能达到与使用723个掩码训练相当水平。结合报告与掩码进一步提升敏感性+13%、特异性+8%，在七种肿瘤中五种超越放射科医生表现。尤为关键的是，R-Super成功实现了脾脏、胆囊、前列腺、膀胱、子宫及食管肿瘤的分割，此前这些部位尚无公开掩码或可用模型。本研究打破‘大规模人工标注不可替代’的传统观念，为多种肿瘤的早期检测提供可扩展、易获取的新路径。相关模型、代码与数据集将发布于https://github.com/MrGiovanni/R-Super。
原文摘要 · Abstract (English)
Early tumor detection save lives. Each year, more than 300 million computed tomography (CT) scans are performed worldwide, offering a vast opportunity for effective cancer screening. However, detecting small or early-stage tumors on these CT scans remains challenging, even for experts. Artificial intelligence (AI) models can assist by highlighting suspicious regions, but training such models typically requires extensive tumor masks--detailed, voxel-wise outlines of tumors manually drawn by radiologists. Drawing these masks is costly, requiring years of effort and millions of dollars. In contrast, nearly every CT scan in clinical practice is already accompanied by medical reports describing the tumor's size, number, appearance, and sometimes, pathology results--information that is rich, abundant, and often underutilized for AI training. We introduce R-Super, which trains AI to segment tumors that match their descriptions in medical reports. This approach scales AI training with large collections of readily available medical reports, substantially reducing the need for manually drawn tumor masks. When trained on 101,654 reports, AI models achieved performance comparable to those trained on 723 masks. Combining reports and masks further improved sensitivity by +13% and specificity by +8%, surpassing radiologists in detecting five of the seven tumor types. Notably, R-Super enabled segmentation of tumors in the spleen, gallbladder, prostate, bladder, uterus, and esophagus, for which no public masks or AI models previously existed. This study challenges the long-held belief that large-scale, labor-intensive tumor mask creation is indispensable, establishing a scalable and accessible path toward early detection across diverse tumor types. We plan to release our trained models, code, and dataset at https://github.com/MrGiovanni/R-Super
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。