ContriMix: Scalable Stain Color Augmentation for Domain Generalization in Digital Pathology
ContriMix introduces a domain label-free stain color augmentation method for digital pathology, outperforming existing methods on the Camelyon17-WILDS dataset.
Integrating Multiscale Topology in Digital Pathology with Pyramidal Graph Convolutional Networks
MS-GCN enhances digital pathology models by integrating multiscale data, improving performance and interpretability.
Efficient Information Fusion for Whole Slide Images
Innovative CDFA-MIL framework enhances feature representation and fusion in digital pathology, setting a new benchmark.
Foundation Models and Information Retrieval in Digital Pathology: Revolutionizing Medical Diagnosis
Foundation models and information retrieval are revolutionizing digital pathology by offering valuable insights through a synergy of large deep models and conventional information retrieval methods.
ContriMix: Scalable Stain Color Augmentation for Domain Generalization in Digital Pathology
ContriMix is a novel domain label-free stain color augmentation method that outperforms competing methods on the Camelyon17-WILDS dataset, improving model performance and generalization in digital pathology.
Rotation-Agnostic Image Representation Learning for Digital Pathology: Efficient and Effective Methods Explored
The author introduces innovative methods, including fast patch selection, lightweight feature extraction, and rotation-agnostic representation learning, to enhance histopathology image analysis efficiency and accuracy.
ContriMix: Scalable Stain Color Augmentation for Domain Generalization in Digital Pathology
ContriMix introduces a domain label-free stain color augmentation method based on DRIT++, enhancing model performance in digital pathology. By leveraging sample stain color variation and random mixing, ContriMix generates synthetic images to improve classifier performance.
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