Feature Re-Embedding: Improving Computational Pathology Performance through Online Instance Feature Fine-Tuning

The core message of this paper is that incorporating an online instance feature re-embedding module, such as the proposed Re-embedded Regional Transformer (R2T), can significantly improve the performance of multiple instance learning (MIL) models in computational pathology tasks by enabling supervised fine-tuning of the instance features.