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DEEP MULTI-MAGNIFICATION NETWORKS FOR MULTI-CLASS IMAGE SEGMENTATION
DEEP MULTI-MAGNIFICATION NETWORKS FOR MULTI-CLASS IMAGE SEGMENTATION
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机译:多级图像分割的深度多放大网络
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摘要
Described herein are Deep Multi -Magnification Networks (DMMNs). The multi-class tissue segmentation architecture processes a set of patches from multiple magnifications to make more accurate predictions. For the supervised training, partial annotations may be used to reduce the burden of annotators. The segmentation architecture with multi-encoder, multi-decoder, and multi-concatenation outperforms other segmentation architectures on breast datasets, and can be used to facilitate pathologists' assessments of breast cancer in margin specimens.
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