前列腺癌病理学的高效自监督分级
Efficient Self-Supervised Grading of Prostate Cancer Pathology
AI总结：
针对前列腺癌全切片图像仅基于切片级标签进行高效 ISUP 分级的挑战，提出基于任务特定自监督学习与序数回归微调的 TSOR 框架，在 PANDA 和 SICAP 数据集上验证了其优于现有方法的有效性。
摘要：
使用 ISUP 系统（国际泌尿病理学会）进行前列腺癌分级以指导治疗决策具有高度主观性，且需要大量专业知识。尽管计算机辅助诊断系统取得了进展，但很少有系统能够仅基于切片级标签，在前列腺活检的全切片图像（WSIs）上实现高效的 ISUP 分级。一些普遍挑战包括管理千兆像素级 WSIs、获取切片级标注以及处理不同中心间的染色变异性。深度学习在 ISUP 分级中面临的主要任务特定挑战之一是，仅基于切片标签学习 Gleason 模式（GPs）的切片级特征。在此背景下，开发了一种高效的 ISUP 分级框架。所提出的 TSOR 基于一种新颖的任务特定自监督学习（SSL）模型，并使用序数回归进行微调。由于训练样本的多样性在 SSL 中起着至关重要的作用，创建了一个在 Gleason 分级（GGs）方面相对均衡的切片级数据集。该均衡数据集用于预训练，使模型能够有效学习 GP 的染色无关特征以实现更好的泛化。在医学图像分级中，期望误分类尽可能接近实际分级。从这一角度出发，随后使用基于序数回归的方法对模型进行 ISUP 分级任务的微调。在最广泛的多中心前列腺活检数据集（PANDA 挑战）以及 SICAP 数据集上的实验结果，证明了这一新颖框架相较于最先进方法的有效性。
英文摘要：
Prostate cancer grading using the ISUP system (International Society of Urological Pathology) for treatment decisions is highly subjective and requires considerable expertise. Despite advances in computer-aided diagnosis systems, few have handled efficient ISUP grading on Whole Slide Images (WSIs) of prostate biopsies based only on slide-level labels. Some of the general challenges include managing gigapixel WSIs, obtaining patch-level annotations, and dealing with stain variability across centers. One of the main task-specific challenges faced by deep learning in ISUP grading, is the learning of patch-level features of Gleason patterns (GPs) based only on their slide labels. In this scenario, an efficient framework for ISUP grading is developed. The proposed TSOR is based on a novel Task-specific Self-supervised learning (SSL) model, which is fine-tuned using Ordinal Regression. Since the diversity of training samples plays a crucial role in SSL, a patch-level dataset is created to be relatively balanced w.r.t. the Gleason grades (GGs). This balanced dataset is used for pre-training, so that the model can effectively learn stain-agnostic features of the GP for better generalization. In medical image grading, it is desirable that misclassifications be as close as possible to the actual grade. From this perspective, the model is then fine-tuned for the task of ISUP grading using an ordinal regression-based approach. Experimental results on the most extensive multicenter prostate biopsies dataset (PANDA challenge), as well as the SICAP dataset, demonstrate the effectiveness of this novel framework compared to state-of-the-art methods.
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。
