A context-aware CNN using 1792x1792 images and spatial feature aggregation outperforms patch-based methods for colorectal cancer grading by 3.61%.
High- throughput adaptive sampling for whole-slide histopathology image analysis (hashi) via convolutional neural networks: Application to in- vasive breast cancer detection
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Context-Aware Convolutional Neural Network for Grading of Colorectal Cancer Histology Images
A context-aware CNN using 1792x1792 images and spatial feature aggregation outperforms patch-based methods for colorectal cancer grading by 3.61%.