Lung Nodule Segmentation

9 papers with code • 5 benchmarks • 2 datasets

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Greatest papers with code

U-Net: Convolutional Networks for Biomedical Image Segmentation

milesial/Pytorch-UNet 18 May 2015

There is large consent that successful training of deep networks requires many thousand annotated training samples.

Cell Segmentation Colorectal Gland Segmentation: +8

Recurrent Residual Convolutional Neural Network based on U-Net (R2U-Net) for Medical Image Segmentation

LeeJunHyun/Image_Segmentation 20 Feb 2018

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.

Image Classification Lesion Segmentation +4

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

MrGiovanni/ModelsGenesis 19 Aug 2019

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 +5

Bi-Directional ConvLSTM U-Net with Densley Connected Convolutions

rezazad68/BCDU-Net 31 Aug 2019

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)

Lesion Segmentation Lung Nodule Segmentation +1

Road Extraction by Deep Residual U-Net

nikhilroxtomar/Deep-Residual-Unet 29 Nov 2017

Road extraction from aerial images has been a hot research topic in the field of remote sensing image analysis.

Lesion Segmentation Lung Nodule Segmentation +3

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

JLiangLab/SemanticGenesis 14 Jul 2020

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 General Classification +6

Level set image segmentation with velocity term learned from data with applications to lung nodule segmentation

notmatthancock/level-set-machine-learning 8 Oct 2019

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.

Lung Nodule Segmentation Semantic Segmentation

iW-Net: an automatic and minimalistic interactive lung nodule segmentation deep network

gmaresta/iW-Net 30 Nov 2018

We propose iW-Net, a deep learning model that allows for both automatic and interactive segmentation of lung nodules in computed tomography images.

Interactive Segmentation Lung Nodule Segmentation

U-Det: A Modified U-Net architecture with bidirectional feature network for lung nodule segmentation

NikV-JS/U-Det 20 Mar 2020

Early diagnosis and analysis of lung cancer involve a precise and efficient lung nodule segmentation in computed tomography (CT) images.

Computed Tomography (CT) Lung Nodule Segmentation