Kernekoncepter
The author proposes a Dynamic Policy-Driven Adaptive Multi-Instance Learning (PAMIL) method for Whole Slide Image (WSI) tasks, integrating dynamic instance sampling and reinforcement learning to improve feature selection and decision-making.
Resumé
The content discusses the limitations of existing Multi-Instance Learning (MIL) methods in histopathology WSI analysis and introduces a novel approach, PAMIL, to address these challenges. By integrating dynamic instance sampling and reinforcement learning, the proposed method outperforms state-of-the-art techniques on datasets related to cancer diagnosis. The study emphasizes the importance of exploring relationships between past knowledge and current instances for accurate predictions in medical imaging.
Statistik
Extensive experiments reveal that our PAMIL method outperforms the state-of-the-art by 3.8% on CAMELYON16 and 4.4% on TCGA lung cancer datasets.
MeanPooling achieved an accuracy of 0.626±0.008 on CAMELYON16.
DSMIL achieved an accuracy of 0.856 on CAMELYON16.
DTFD achieved an accuracy of 0.908±0.013 on CAMELYON16.
Citater
"To break free from limitations, we integrate dynamic instance sampling and reinforcement learning into a unified framework."
"Our proposed PAMIL method significantly outperforms current state-of-the-art (SOTA) methods."
"Exploring interrelationships between instance sampling, feature representation, and decision-making is crucial for accurate inference."