论文标题
与Champkit评估组织病理学转移学习
Evaluating histopathology transfer learning with ChampKit
论文作者
论文摘要
组织病理学仍然是各种癌症诊断的黄金标准。计算机视觉的最新进展，特别是深度学习，促进了针对各种任务的组织病理学图像的分析，包括免疫细胞检测和微卫星不稳定性分类。每个任务的最新工作通常采用验证的基础体系结构，这些架构已鉴定在Imagenet上的图像分类。开发组织病理学分类器的标准方法倾向于将重点放在优化单个任务的模型上，而不是考虑建模创新的各个方面，这些创新可以改善跨任务的概括。在这里，我们提出了Champkit（模型预测工具包的全面组织病理学评估）：可扩展的，完全可重现的基准测试工具包，由大量的斑点级图像分类任务组成，跨不同的癌症。 Champkit能够系统地记录模型和方法中提议改进的性能影响的一种方法。 Champkit源代码和数据可在https://github.com/kaczmarj/champkit中自由访问。
Histopathology remains the gold standard for diagnosis of various cancers. Recent advances in computer vision, specifically deep learning, have facilitated the analysis of histopathology images for various tasks, including immune cell detection and microsatellite instability classification. The state-of-the-art for each task often employs base architectures that have been pretrained for image classification on ImageNet. The standard approach to develop classifiers in histopathology tends to focus narrowly on optimizing models for a single task, not considering the aspects of modeling innovations that improve generalization across tasks. Here we present ChampKit (Comprehensive Histopathology Assessment of Model Predictions toolKit): an extensible, fully reproducible benchmarking toolkit that consists of a broad collection of patch-level image classification tasks across different cancers. ChampKit enables a way to systematically document the performance impact of proposed improvements in models and methodology. ChampKit source code and data are freely accessible at https://github.com/kaczmarj/champkit .
