
The author explores reducing the complexity of self-supervised learning to enhance weakly-supervised classification performance in computational pathology, focusing on adaptations to improve efficiency and accessibility.


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reduced-self-supervised-learning-complexity-in-computational-pathology-enhances-weakly-supervised-classification-performance


Reduced Self-Supervised Learning Complexity in Computational Pathology Enhances Weakly-Supervised Classification Performance


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自己監督学習の複雑さを削減し、計算病理学における弱教師付き分類の性能を向上させる


reduced-self-supervised-learning-complexity-in-computational-pathology-for-improved-weakly-supervised-classification-performance


Reduced self-supervised learning complexity in computational pathology for improved weakly-supervised classification performance
