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 .
翻译:最近在计算机视觉方面的进步,特别是深层次的学习,促进了对各种任务,包括免疫细胞检测和微型卫星不稳定性分类的生理病理学图像的分析。每项任务的最新技术通常都使用在图象网图像分类方面受过预先培训的基础结构。发展组织病理学分类师的标准方法往往狭隘地侧重于优化单一任务的模型,而没有考虑改进各项任务总体化的模型化创新的各个方面。这里我们介绍ChamKit(模型预测工具综合病理学评估):一个可推广的、完全复制的基准工具包,其中包括广泛收集不同癌症的近距离图像分类任务。CampKit为系统记录模型和方法拟议改进的绩效影响提供了一种方法。CampKit源代码和数据可在https://github.com/kaczmarj/champkit免费查阅。