9 papers with code • 5 benchmarks • 2 datasets
There is large consent that successful training of deep networks requires many thousand annotated training samples.
Ranked #1 on Medical Image Segmentation on ISBI 2012 EM Segmentation (Warping Error metric)
In this paper, we propose a Recurrent Convolutional Neural Network (RCNN) based on U-Net as well as a Recurrent Residual Convolutional Neural Network (RRCNN) based on U-Net models, which are named RU-Net and R2U-Net respectively.
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 strengthen feature propagation and encourage feature reuse, we use densely connected convolutions in the last convolutional layer of the encoding path.
Ranked #1 on Lesion Segmentation on ISIC 2018 (F1-Score metric)
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
Approach: We introduce an extension of the standard level set image segmentation method where the velocity function is learned from data via machine learning regression methods, rather than a priori designed.
We propose iW-Net, a deep learning model that allows for both automatic and interactive segmentation of lung nodules in computed tomography images.
Early diagnosis and analysis of lung cancer involve a precise and efficient lung nodule segmentation in computed tomography (CT) images.