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More importantly, learning a model from scratch simply in 3D may not necessarily yield performance better than transfer learning from ImageNet in 2D, but our Models Genesis consistently top any 2D approaches including fine-tuning the models pre-trained from ImageNet as well as fine-tuning the 2D versions of our Models Genesis, confirming the importance of 3D anatomical information and significance of our Models Genesis for 3D medical imaging.
Ranked #1 on Lung Nodule Segmentation on LIDC-IDRI
To this end, we train deep models to learn semantically enriched visual representation by self-discovery, self-classification, and self-restoration of the anatomy underneath medical images, resulting in a semantics-enriched, general-purpose, pre-trained 3D model, named Semantic Genesis.
Ranked #1 on Lung Nodule Detection on LUNA2016 FPRED
Recently deep learning has been witnessing widespread adoption in various medical image applications.
Automatic and accurate lung nodule detection from 3D Computed Tomography scans plays a vital role in efficient lung cancer screening.