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PCAF‐Net: A liver segmentation network based on deep learning

https://doi.org/10.1049/ipr2.12346
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1002/cnm.2765openalex-snapshot
Fully automated liver segmentation using Sobolev gradient‐based level set evolution
inferredcites →https://doi.org/10.1007/978-3-030-01234-2_49openalex-snapshot
Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation
inferredcites →https://doi.org/10.1007/978-3-030-01249-6_17openalex-snapshot
ExFuse: Enhancing Feature Fusion for Semantic Segmentation
inferredcites →https://doi.org/10.1007/978-3-319-24574-4_28openalex-snapshot
U-Net: Convolutional Networks for Biomedical Image Segmentation
inferredcites →https://doi.org/10.1007/978-3-319-28194-0_7openalex-snapshot
Automatic Liver Tumor Segmentation in Follow-Up CT Scans: Preliminary Method and Results
inferredcites →https://doi.org/10.1007/s00530-020-00709-xopenalex-snapshot
Automatic liver segmentation from abdominal CT volumes using improved convolution neural networks
inferredcites →https://doi.org/10.1007/s11760-017-1200-xopenalex-snapshot
Honoring Murat Kunt
inferredcites →https://doi.org/10.1007/s11760-020-01835-9openalex-snapshot
MSN-Net: a multi-scale context nested U-Net for liver segmentation
inferredcites →https://doi.org/10.1016/j.artmed.2021.102023openalex-snapshot
Liver segmentation in abdominal CT images via auto-context neural network and self-supervised contour attention
inferredcites →https://doi.org/10.1016/j.cmpb.2007.07.005openalex-snapshot
Efficient liver segmentation using a level-set method with optimal detection of the initial liver boundary from level-set speed images
inferredcites →https://doi.org/10.1016/j.cmpb.2013.08.019openalex-snapshot
A hybrid semi-automatic method for liver segmentation based on level-set methods using multiple seed points
inferredcites →https://doi.org/10.1016/j.ejrad.2020.108918openalex-snapshot
Validation of a fully automated liver segmentation algorithm using multi-scale deep reinforcement learning and comparison versus manual segmentation
inferredcites →https://doi.org/10.1109/cits.2017.8035318openalex-snapshot
Texture-based treatment prediction by automatic liver tumor segmentation on computed tomography
inferredcites →https://doi.org/10.1109/cvpr.2015.7298594openalex-snapshot
Going deeper with convolutions
inferredcites →https://doi.org/10.1109/cvpr.2015.7298965openalex-snapshot
Fully convolutional networks for semantic segmentation
inferredcites →https://doi.org/10.1109/cvpr.2016.308openalex-snapshot
Rethinking the Inception Architecture for Computer Vision
inferredcites →https://doi.org/10.1109/cvpr.2017.106openalex-snapshot
Feature Pyramid Networks for Object Detection
inferredcites →https://doi.org/10.1109/cvpr.2017.660openalex-snapshot
Pyramid Scene Parsing Network
inferredcites →https://doi.org/10.1109/cvpr.2018.00745openalex-snapshot
Squeeze-and-Excitation Networks
inferredcites →https://doi.org/10.1109/cvpr.2019.00326openalex-snapshot
Dual Attention Network for Scene Segmentation
inferredcites →https://doi.org/10.1109/icassp40776.2020.9053405openalex-snapshot
UNet 3+: A Full-Scale Connected UNet for Medical Image Segmentation
inferredcites →https://doi.org/10.1109/icassp40776.2020.9053454openalex-snapshot
Lightweight V-Net for Liver Segmentation
inferredcites →https://doi.org/10.1109/itme.2008.4743956openalex-snapshot
F-score feature selection method may improve texture-based liver segmentation strategies
inferredcites →https://doi.org/10.1109/tbme.2013.2267212openalex-snapshot
A Likelihood and Local Constraint Level Set Model for Liver Tumor Segmentation from CT Volumes
inferredcites →https://doi.org/10.1109/tip.2015.2481326openalex-snapshot
Automatic Liver Segmentation Based on Shape Constraints and Deformable Graph Cut in CT Images
inferredcites →https://doi.org/10.1109/tmi.2018.2845918openalex-snapshot
H-DenseUNet: Hybrid Densely Connected UNet for Liver and Tumor Segmentation From CT Volumes
inferredcites →https://doi.org/10.1109/tmi.2019.2959609openalex-snapshot
UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentation
inferredcites →https://doi.org/10.1109/tpami.2015.2389824openalex-snapshot
Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition
inferredcites →https://doi.org/10.1109/tpami.2016.2644615openalex-snapshot
SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
inferredcites →https://doi.org/10.1117/1.jmi.2.3.034502openalex-snapshot
Automated method for detection and segmentation of liver metastatic lesions in follow-up CT examinations
inferredcites →https://doi.org/10.1117/12.710422openalex-snapshot
A comparison of texture models for automatic liver segmentation
inferredcites →https://doi.org/10.1609/aaai.v31i1.11231openalex-snapshot
Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning
inferredcites →https://doi.org/10.3389/fbioe.2020.605132openalex-snapshot
RA-UNet: A Hybrid Deep Attention-Aware Network to Extract Liver and Tumor in CT Scans
inferredcites →https://doi.org/10.3389/fgene.2019.01110openalex-snapshot
Channel-Unet: A Spatial Channel-Wise Convolutional Neural Network for Liver and Tumors Segmentation
inferredcites →https://doi.org/10.4018/ijehmc.2021010103openalex-snapshot
Cascaded Dilated Deep Residual Network for Volumetric Liver Segmentation From CT Image
inferredcites →https://doi.org/10.48550/arxiv.1502.03167openalex-snapshot
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
inferredcites →https://doi.org/10.48550/arxiv.2006.11538openalex-snapshot
Pyramidal Convolution: Rethinking Convolutional Neural Networks for Visual Recognition

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.2139/ssrn.4689418openalex-extracted
Mpaf-Net: Medical Image Segmentation Based on Multi-Scale Attention Fusion Convolutional Neural Network
inferred← citeshttps://doi.org/10.1007/s11042-026-21424-1openalex-snapshot
SEALS-Net: hybrid loss-guided segmentation and uncertainty based bayesian GRU classification for liver tumor
inferred← citeshttps://doi.org/10.1007/s42979-024-02719-2openalex-snapshot
Automatic Liver Segmentation from Multiphase CT Using Modified SegNet and ASPP Module
inferred← citeshttps://doi.org/10.1016/j.compbiomed.2024.109459openalex-snapshot
Challenges and solutions of deep learning-based automated liver segmentation: A systematic review
inferred← citeshttps://doi.org/10.1049/ipr2.12708openalex-snapshot
Dual‐ and triple‐stream RESUNET/UNET architectures for multi‐modal liver segmentation
inferred← citeshttps://doi.org/10.1109/icsgrc62081.2024.10690868openalex-snapshot
Use of Deep Learning for Liver Segmentation During Laparoscopic Cholecystectomy
inferred← citeshttps://doi.org/10.7717/peerj-cs.3859openalex-snapshot
A systematic review of machine learning and deep learning approaches for gastrointestinal cancer diagnosis
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