Lung Nodule Detection

6 papers with code • 1 benchmarks • 2 datasets

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Most implemented papers

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

uci-cbcl/DeepEM-for-Weakly-Supervised-Detection 14 May 2018

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

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.

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.

A Systematic Analysis for State-of-the-Art 3D Lung Nodule Proposals Generation

extendedcaffe/extended-caffe 9 Jan 2018

Lung nodule proposals generation is the primary step of lung nodule detection and has received much attention in recent years .

DeepSEED: 3D Squeeze-and-Excitation Encoder-Decoder Convolutional Neural Networks for Pulmonary Nodule Detection

ymli39/DeepSEED-3D-ConvNets-for-Pulmonary-Nodule-Detection 6 Apr 2019

Pulmonary nodule detection plays an important role in lung cancer screening with low-dose computed tomography (CT) scans.

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

HiLab-git/SCPM-Net 12 Apr 2021

To overcome these problems, we propose a 3D sphere representation-based center-points matching detection network that is anchor-free and automatically predicts the position, radius, and offset of nodules without the manual design of nodule/anchor parameters.