Benchmarks
| TREND | DATASET | BEST METHOD | PAPER TITLE | PAPER | CODE | COMPARE |
|---|---|---|---|---|---|---|
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Datasets
Greatest papers with code
Models Genesis: Generic Autodidactic Models for 3D Medical Image Analysis
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
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
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
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
• wentaozhu/DeepEM-for-Weakly-Supervised-Detection
• 
Recently deep learning has been witnessing widespread adoption in various medical image applications.
SCPM-Net: An Anchor-free 3D Lung Nodule Detection Network using Sphere Representation and Center Points Matching
Automatic and accurate lung nodule detection from 3D Computed Tomography scans plays a vital role in efficient lung cancer screening.