由于图像注释量不足,计算组织病理学中的人工智能通常依赖于微调预训练的神经网络。虽然香草微调已经证明是有效的,计算机视觉研究最近提出了改进算法,有希望更好的准确性。虽然最初的研究已经证明了这些算法对医疗人工智能的好处,特别是放射学,没有经验证据可以提高组织病理学的准确性。因此,基于ConvNeXt架构,我们的研究对九种任务适应技术进行了系统比较,即,DELTA,L2-SP,MARS-PGM,双向调谐,BSS,MultiTune,SpotTune,协调,和香草微调,使用八个数据集的五个组织病理学分类任务。结果基于外部测试和统计验证,并揭示了多方面的情况:某些技术比其他技术更适合组织病理学,但是根据分类任务,与控制方法相比,五种先进的任务适应技术在精度上有了显著的相对提高,即,香草微调(例如,共调谐:P(200nm)=0.942,d=2.623)。此外,我们研究了九种方法中的三种方法相对于训练集大小的分类精度(例如,共调谐:P(200nm)=0.951,γ=0.748。总的来说,我们的研究结果表明,高级任务适应技术在组织病理学中的性能受到影响因素的影响,例如特定的分类任务或训练数据集的大小。
Due to an insufficient amount of image annotation, artificial intelligence in computational histopathology usually relies on fine-tuning pre-trained neural networks. While vanilla fine-tuning has shown to be effective, research on computer vision has recently proposed improved algorithms, promising better accuracy. While initial studies have demonstrated the benefits of these algorithms for medical AI, in particular for radiology, there is no empirical evidence for improved accuracy in histopathology. Therefore, based on the ConvNeXt architecture, our study performs a systematic comparison of nine task adaptation techniques, namely, DELTA, L2-SP, MARS-PGM, Bi-Tuning, BSS, MultiTune, SpotTune, Co-Tuning, and vanilla fine-tuning, on five histopathological classification tasks using eight datasets. The results are based on external testing and statistical validation and reveal a multifaceted picture: some techniques are better suited for histopathology than others, but depending on the classification task, a significant relative improvement in accuracy was observed for five advanced task adaptation techniques over the control method, i.e., vanilla fine-tuning (e.g., Co-Tuning: P(≫) = 0.942, d = 2.623). Furthermore, we studied the classification accuracy for three of the nine methods with respect to the training set size (e.g., Co-Tuning: P(≫) = 0.951, γ = 0.748). Overall, our results show that the performance of advanced task adaptation techniques in histopathology is affected by influencing factors such as the specific classification task or the size of the training dataset.