medical-7

Lung Nodule Segmentation

3260 papers • 126 benchmarks • 313 datasets

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Benchmarks

These leaderboards are used to track progress in lung-nodule-segmentation-14

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Dataset

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LUNA

LIDC-IDRI

NIH

Libraries

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Use these libraries to find lung-nodule-segmentation-14 models and implementations

rezazad68/BCDU-Net

2 papers 667

Datasets

LIDC-IDRI

LUNA

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Most implemented papers

Road Extraction by Deep Residual U-Net

Zhengxin Zhang, Qingjie Liu, Yunhong Wang•Tue Nov 28 2017

A semantic segmentation neural network, which combines the strengths of residual learning and U-Net, is proposed for road area extraction, which outperforms all the comparing methods and demonstrates its superiority over recently developed state of the arts methods.

2453

Content

Montgomery County

Lung Nodule

nikhilroxtomar/semantic-segmentatio…

2 papers 135

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

Md. Zahangir Alom, T. Taha, C. Yakopcic, Mahmudul Hasan, V. Asari•Mon Feb 19 2018

A Recurrent Convolutional Neural Network (RCNN) based on U-Net as well as a Recurrent Residual convolutional neural Network (RRCNN), which are named RU-Net and R2U-Net respectively are proposed, which show superior performance on segmentation tasks compared to equivalent models including U-nets and residual U- net.

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Bi-Directional ConvLSTM U-Net with Densley Connected Convolutions

Sergio Escalera, Reza Azad, M. Fathy, Maryam Asadi-Aghbolaghi•Fri Aug 30 2019

This paper proposes an extension of U-Net, Bi-directional ConvLSTM U- net with Densely connected convolutions (BCDU-Net), for medical image segmentation, in which the full advantages of U -Net, bi- directional Conv lSTM (BConvL STM) and the mechanism of dense convolutions are taken.

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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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Uncertainty-Guided Lung Nodule Segmentation with Feature-Aware Attention

Han Yang, Lu Shen, Mengke Zhang, Qiuli Wang•Sat Oct 23 2021

An Uncertainty-Guided Segmentation Network (UGS-Net) is proposed, which learns the rich visual features from the regions that may cause segmentation uncertainty and contributes to a better segmentation result.

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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.

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

B. van Ginneken, C. Jacobs, Guilherme Aresta, António Cunha, A. Campilho, Teresa Araújo, Isabel Ramos•Thu Nov 29 2018

It is shown that iW-Net allows to correct the segmentation of small nodules, essential for proper patient referral decision, as well as improve the segmentations of the challenging non-solid nodules and thus may be an important tool for increasing the early diagnosis of lung cancer.

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

Nikhil Varma Keetha, P. SamsonAnoshBabu, Chandra Sekhara Rao Annavarapu•Thu Mar 19 2020

U-Det, a resource-efficient model architecture, which is an end to end deep learning approach to solve the task at hand, incorporates a Bi-FPN (bidirectional feature network) between the encoder and decoder and uses Mish activation function and class weights of masks to enhance segmentation efficiency.

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Probabilistic 3D segmentation for aleatoric uncertainty quantification in full 3D medical data

F. V. D. Sommen, P. D. With, C.G.A. Viviers, Amaan Valiuddin•Thu Apr 06 2023

A 3D probabilistic segmentation framework augmented with NFs, to enable capturing the distributions of various complexity, and is the first to present a 3D Squared Generalized Energy Distance (D2 GED) of 0.401 and a high 0.468 Hungarian-matched 3D IoU.

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