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Greatest papers with code

Models Genesis: Generic Autodidactic Models for 3D Medical Image Analysis

19 Aug 2019MrGiovanni/ModelsGenesis

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.

BRAIN TUMOR SEGMENTATION LIVER SEGMENTATION LUNG NODULE DETECTION LUNG NODULE SEGMENTATION PULMONARY EMBOLISM DETECTION SELF-SUPERVISED LEARNING TRANSFER LEARNING

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

14 Jul 2020JLiangLab/SemanticGenesis

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.

BRAIN TUMOR SEGMENTATION CLASSIFICATION LIVER SEGMENTATION LUNG NODULE DETECTION LUNG NODULE SEGMENTATION REPRESENTATION LEARNING SELF-SUPERVISED LEARNING TRANSFER LEARNING

DeepEM: Deep 3D ConvNets With EM For Weakly Supervised Pulmonary Nodule Detection

14 May 2018wentaozhu/DeepEM-for-Weakly-Supervised-Detection

Recently deep learning has been witnessing widespread adoption in various medical image applications.

COMPUTED TOMOGRAPHY (CT) LUNG NODULE DETECTION

SCPM-Net: An Anchor-free 3D Lung Nodule Detection Network using Sphere Representation and Center Points Matching

12 Apr 2021HiLab-git/SCPM-Net

Automatic and accurate lung nodule detection from 3D Computed Tomography scans plays a vital role in efficient lung cancer screening.

LUNG NODULE DETECTION