Department of Pathology, Radboud University Medical Center, Nijmegen, The Netherlands.
Center for Medical Image Science and Visualization, Linköping University, Linköping, Sweden.
Gigascience. 2022 Jun 14;11. doi: 10.1093/gigascience/giac056.
In the last decade, the field of computational pathology has advanced at a rapid pace because of the availability of deep neural networks, which achieved their first successes in computer vision tasks in 2012. An important driver for the progress of the field were public competitions, so called 'Grand Challenges', in which increasingly large data sets were offered to the public to solve clinically relevant tasks. Going from the first Pathology challenges, which had data obtained from 23 patients, to current challenges sharing data of thousands of patients, performance of developed deep learning solutions has reached (and sometimes surpassed) the level of experienced pathologists for specific tasks. We expect future challenges to broaden the horizon, for instance by combining data from radiology, pathology and tumor genetics, and to extract prognostic and predictive information independent of currently used grading schemes.
在过去的十年中，由于深度学习网络的出现，计算病理学领域发展迅速，这些网络在 2012 年首次在计算机视觉任务中取得成功。该领域的一个重要推动因素是公共竞赛，即所谓的“大挑战”，其中越来越多的大型数据集向公众提供，以解决与临床相关的任务。从最初的 Pathology 挑战赛，即从 23 名患者获得的数据，到当前的挑战赛分享数千名患者的数据，开发的深度学习解决方案的性能已经达到（有时甚至超过）特定任务中经验丰富病理学家的水平。我们预计未来的挑战将拓宽视野，例如结合放射学、病理学和肿瘤遗传学的数据，并提取与当前使用的分级方案无关的预后和预测信息。
