Learning Semantics-enriched Representation via Self-discovery, Self-classification, and Self-restoration

Medical images are naturally associated with rich semantics about the human anatomy, reflected in an abundance of recurring anatomical patterns, offering unique potential to foster deep semantic representation learning and yield semantically more powerful models for different medical applications. But how exactly such strong yet free semantics embedded in medical images can be harnessed for self-supervised learning remains largely unexplored... (read more)

PDF Abstract
TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK RESULT BENCHMARK
Brain Tumor Segmentation BRATS-2013 Semantic Genesis Dice Score 92.76 # 1
Brain Tumor Segmentation BRATS 2018 Semantic Genesis IoU 68.8 # 1
Lung Nodule Segmentation LIDC-IDRI Semantic Genesis IoU 77.24 # 2
Liver Segmentation LiTS2017 Semantic Genesis IoU 85.6 # 2
Dice 92.27 # 2
Lung Nodule Detection LUNA2016 FPRED Semantic Genesis AUC 98.47 # 1

Methods used in the Paper


METHOD TYPE
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