While Multiple Instance Learning (MIL) has shown promising results in digital Pathology Whole Slide Image (WSI) classification, such a paradigm still faces performance and generalization problems due to challenges in high computational costs on Gigapixel WSIs and limited sample size for model training. To deal with the computation problem, most MIL methods utilize a frozen pretrained model from ImageNet to obtain representations first. This process may lose essential information owing to the large domain gap and hinder the generalization of model due to the lack of image-level training-time augmentations. Though Self-supervised Learning (SSL) proposes viable representation learning schemes, the improvement of the downstream task still needs to be further explored in the conversion from the task-agnostic features of SSL to the task-specifics under the partial label supervised learning. To alleviate the dilemma of computation cost and performance, we propose an efficient WSI fine-tuning framework motivated by the Information Bottleneck theory. The theory enables the framework to find the minimal sufficient statistics of WSI, thus supporting us to fine-tune the backbone into a task-specific representation only depending on WSI-level weak labels. The WSI-MIL problem is further analyzed to theoretically deduce our fine-tuning method. Our framework is evaluated on five pathology WSI datasets on various WSI heads. The experimental results of our fine-tuned representations show significant improvements in both accuracy and generalization compared with previous works. Source code will be available at https://github.com/invoker-LL/WSI-finetuning.

翻译：尽管多实例学习（Multiple Instance Learning, MIL）在数字病理全切片图像（Whole Slide Image, WSI）分类中取得了显著成效，但由于十亿像素级WSI的高计算成本以及训练样本规模有限带来的挑战，该范式仍面临性能和泛化问题。为解决计算问题，多数MIL方法首先采用ImageNet预训练模型的冻结参数获取表征。该过程因较大领域差异可能丢失关键信息，且因缺乏图像级训练时增强而阻碍模型泛化。尽管自监督学习（Self-supervised Learning, SSL）提出了可行的表征学习方案，但将SSL的任务无关特征转化为部分标签监督学习下的任务特定特征时，下游任务的提升仍需进一步探索。为缓解计算成本与性能之间的困境，我们受信息瓶颈理论启发，提出一种高效的WSI微调框架。该理论使框架能够寻找WSI的最小充分统计量，从而仅依赖WSI级弱标签将主干网络微调为任务特定表征。进一步通过分析WSI-MIL问题在理论上推演了我们的微调方法。该框架在五种病理WSI数据集上对不同WSI头部进行了评估。与先前工作相比，我们微调表征的实验结果在准确率和泛化性方面均显示出显著提升。源代码将发布于https://github.com/invoker-LL/WSI-finetuning。