Temel Kavramlar
Topological regularization enhances MIL performance in data-scarce scenarios.
Özet
Abstract
MIL models are crucial in biomedical data analysis.
Topological regularization improves MIL performance in data-scarce scenarios.
Introduction
MIL assigns a single label to a bag of instances.
MIL classifiers require substantial training data.
Method
TR-MIL introduces topological regularization to enhance MIL performance.
Topological algorithms provide inductive biases for data structure.
Experiments
TR-MIL shows improvement across various datasets.
Synthetic datasets demonstrate the robustness of TR-MIL.
Anemia classification benefits from topological regularization.
Conclusion
TR-MIL leverages geometrical-topological properties for MIL frameworks.
Impact Statement
Ethical implications of the methodology are considered.
İstatistikler
우리의 방법은 MIL 벤치마크에서 2.8%의 평균 향상을 보여줍니다.
합성 MIL 데이터 세트에서 15.3%의 향상을 보여줍니다.
실제 바이오의학 데이터 세트에서 5.5%의 향상을 보여줍니다.
Alıntılar
"Topological regularization ensures preservation of geometrical-topological information in the latent space."
"TR-MIL outperforms state-of-the-art methods in MIL benchmarks."