PDF(Pubmed)
Abstract：
Burkitt Lymphoma (BL) is a highly treatable cancer. However, delayed diagnosis of BL contributes to high mortality in BL endemic regions of Africa. Lack of enough pathologists in the region is a major reason for delayed diagnosis. The work described in this paper is a proof-of-concept study to develop a targeted, open access AI tool for screening of histopathology slides in suspected BL cases. Slides were obtained from a total of 90 BL patients. 70 Tonsillectomy samples were used as controls. We fine-tuned 6 pre-trained models and evaluated the performance of all 6 models across different configurations. An ensemble-based consensus approach ensured a balanced and robust classification. The tool applies novel features to BL diagnosis including use of multiple image magnifications, thus enabling use of different magnifications of images based on the microscope/scanner available in remote clinics, composite scoring of multiple models and utilizing MIL with weak labeling and image augmentation, enabling use of relatively low sample size to achieve good performance on the inference set. The open access model allows free access to the AI tool from anywhere with an internet connection. The ultimate aim of this work is making pathology services accessible, efficient and timely in remote clinics in regions where BL is endemic. New generation of low-cost slide scanners/microscopes is expected to make slide images available immediately for the AI tool for screening and thus accelerate diagnosis by pathologists available locally or online.
摘要：
伯基特淋巴瘤（BL）是一种高度可治疗的癌症。然而,BL的延迟诊断导致非洲BL流行地区的高死亡率.该地区缺乏足够的病理学家是延迟诊断的主要原因。本文所描述的工作是一项概念验证研究，旨在开发一种有针对性的，开放式AI工具，用于筛查可疑BL病例的组织病理学切片。从总共90名BL患者获得载玻片。使用70个扁桃体切除术样品作为对照。我们对6个预训练模型进行了微调，并评估了所有6个模型在不同配置中的性能。基于集合的共识方法确保了平衡和稳健的分类。该工具将新颖的特征应用于BL诊断，包括使用多个图像放大倍数，因此，可以根据远程诊所提供的显微镜/扫描仪使用不同的图像放大倍数，对多个模型进行综合评分，并利用具有弱标记和图像增强的MIL，允许使用相对较低的样本量来在推理集上实现良好的性能。开放访问模型允许从任何具有互联网连接的地方免费访问AI工具。这项工作的最终目的是使病理服务变得容易获得，在BL流行地区的偏远诊所中高效及时。新一代低成本的载玻片扫描仪/显微镜有望使载玻片图像立即可用于AI工具进行筛查，从而加速本地或在线病理学家的诊断。
