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computer-vision-3

Lung Nodule Detection

3260 papers • 126 benchmarks • 313 datasets

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Benchmarks

These leaderboards are used to track progress in lung-nodule-detection-6

Trend

Dataset

Best Model

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LUNA2016 FPRED

LIDC-IDRI

Libraries

Use these libraries to find lung-nodule-detection-6 models and implementations

Datasets

LIDC-IDRI

LUNA

LUNA16

Subtasks

Lung Nodule 3D Detection

Most implemented papers

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

Xiaohui Xie, Wentao Zhu, Y. S. Vang, Yufang Huang•Sun May 13 2018

This work proposes DeepEM, a novel deep 3D ConvNet framework augmented with expectation-maximization (EM), to mine weakly supervised labels in EMRs for pulmonary nodule detection, and results show that DeepEM can lead to 1.5% and 3.9% average improvement in free-response receiver operating characteristic (FROC) scores on LUNA16 and Tianchi datasets, respectively, demonstrating the utility of incomplete information in E MRs for improving deep learning algorithms.

Content

Introduction Benchmarks Datasets Subtasks Libraries Papers

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

Yuemeng Li, Yang Fan•Fri Apr 05 2019

A novel deep 3D convolutional neural network with an Encoder-Decoder structure in conjunction with a region proposal network that adopts the squeeze-and-excitation structure to learn effective image features and utilize inter-dependency information of different feature maps.

92 0

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

Zongwei Zhou, M. R. Siddiquee, Nima Tajbakhsh, Jianming Liang, V. Sodha, Ruibin Feng, M. Gotway•Sun Aug 18 2019

The authors' extensive experiments demonstrate that their Models Genesis significantly outperform learning from scratch in all five target 3D applications covering both segmentation and classification, and are attributed to the unified self-supervised learning framework, built on a simple yet powerful observation.

364 0

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

Jianming Liang, M. Gotway, F. Haghighi, M. Taher, Zongwei Zhou•Mon Jul 13 2020

This work trains 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.

68 0

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

Hui Wu, Jian Tang, Matrix Yao, A. Hu, Gaofeng Sun, Xiaokun Yu•Mon Jan 08 2018

This paper first constructs a model of 3-dimension Convolutional Neural Network (3D CNN) to generate lung nodule proposals, which can achieve the state-of-the-art performance, and implements the model on CPU platform and proposes an Intel Extended-Caffe framework which supports many highly-efficient 3D computations.

5 0

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Lung nodule detection and classification from Thorax CT-scan using RetinaNet with transfer learning

Ivan William Harsono, Suryadiputra Liawatimena, T. W. Cenggoro•Tue Mar 31 2020

I3DR-Net successfully outperform previous state-of-the-art Retina U-Net and U-FRCNN + mean average precision (mAP) by 7.9% and 7.2% for malignant nodule detection and classification task.

103 0

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

Dimitris N. Metaxas, Jieneng Chen, Guotai Wang, Shaoting Zhang, Xiangde Luo, Tao Song, Yinan Chen, Kang Li•Sun Apr 11 2021

A 3D sphere representation-based center-points matching detection network (SCPM-Net) that is anchor-free and automatically predicts the position, radius, and offset of nodules without manual design of nodule/anchor parameters is proposed.

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A systematic approach to deep learning-based nodule detection in chest radiographs

Finn Behrendt, M. Bengs, Debayan Bhattacharya, J. Krüger, R. Opfer, A. Schlaefer•Tue Jun 20 2023

A systematic comparison of state-of-the-art object detection algorithms for the task of lung nodule detection is presented and the critical aspect of class imbalance is addressed and a data augmentation approach as well as transfer learning to boost performance is demonstrated.

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Lung Nodule Detection | State-of-the-Art