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Automatic Pancreas Segmentation Using Coarse-Scaled 2D Model of Deep Learning: Usefulness of Data Augmentation and Deep U-Net

https://doi.org/10.3390/app10103360
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What this work says

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

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Empirical Risk Minimization
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Fully Automated Pancreas Segmentation with Two-Stage 3D Convolutional Neural Networks
inferredcites →https://doi.org/10.1007/978-3-319-24553-9_68openalex-snapshot
DeepOrgan: Multi-level Deep Convolutional Networks for Automated Pancreas 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/10.1007/978-3-319-66182-7_79openalex-snapshot
A Fixed-Point Model for Pancreas Segmentation in Abdominal CT Scans
inferredcites →https://doi.org/10.1007/s10278-013-9622-7openalex-snapshot
The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository
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Deep Learning Techniques for Medical Image Segmentation: Achievements and Challenges
inferredcites →https://doi.org/10.1016/j.acra.2019.05.016openalex-snapshot
Quantitative and Qualitative Evaluation of Convolutional Neural Networks with a Deeper U-Net for Sparse-View Computed Tomography Reconstruction
inferredcites →https://doi.org/10.1016/j.cmpb.2019.07.002openalex-snapshot
Automated pancreas segmentation from computed tomography and magnetic resonance images: A systematic review
inferredcites →https://doi.org/10.1016/j.compbiomed.2019.103438openalex-snapshot
Automatic segmentation of the uterus on MRI using a convolutional neural network
inferredcites →https://doi.org/10.1016/j.compbiomed.2020.103698openalex-snapshot
Improving breast mass classification by shared data with domain transformation using a generative adversarial network
inferredcites →https://doi.org/10.1016/j.compbiomed.2020.103767openalex-snapshot
Bone segmentation on whole-body CT using convolutional neural network with novel data augmentation techniques
inferredcites →https://doi.org/10.1016/j.compmedimag.2019.04.004openalex-snapshot
Morphological and multi-level geometrical descriptor analysis in CT and MRI volumes for automatic pancreas segmentation
inferredcites →https://doi.org/10.1016/j.media.2012.08.002openalex-snapshot
Multi-shape graph cuts with neighbor prior constraints and its application to lung segmentation from a chest CT volume
inferredcites →https://doi.org/10.1016/j.media.2018.01.006openalex-snapshot
Spatial aggregation of holistically-nested convolutional neural networks for automated pancreas localization and segmentation
inferredcites →https://doi.org/10.1038/s41591-019-0447-xopenalex-snapshot
End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography
inferredcites →https://doi.org/10.1038/s41598-019-52737-xopenalex-snapshot
Data augmentation using generative adversarial networks (CycleGAN) to improve generalizability in CT segmentation tasks
inferredcites →https://doi.org/10.1056/nejmoa1102873openalex-snapshot
Reduced Lung-Cancer Mortality with Low-Dose Computed Tomographic Screening
inferredcites →https://doi.org/10.1109/cvpr.2019.00020openalex-snapshot
AutoAugment: Learning Augmentation Strategies From Data
inferredcites →https://doi.org/10.1109/tcsvt.2019.2935128openalex-snapshot
Data Augmentation Using Random Image Cropping and Patching for Deep CNNs
inferredcites →https://doi.org/10.1109/tmi.2019.2940555openalex-snapshot
Automated Muscle Segmentation from Clinical CT Using Bayesian U-Net for Personalized Musculoskeletal Modeling
inferredcites →https://doi.org/10.1109/tmi.2019.2948320openalex-snapshot
Modified U-Net (mU-Net) With Incorporation of Object-Dependent High Level Features for Improved Liver and Liver-Tumor Segmentation in CT Images
inferredcites →https://doi.org/10.3322/caac.21551openalex-snapshot
Cancer statistics, 2019
inferredcites →https://doi.org/10.48550/arxiv.1707.04912openalex-snapshot
Improving Deep Pancreas Segmentation in CT and MRI Images via Recurrent Neural Contextual Learning and Direct Loss Function
inferredcites →https://doi.org/10.7937/k9/tcia.2016.tnb1kqbuopenalex-snapshot
Data From Pancreas-CT

What points at this work

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

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Automated Detection of Pancreatic Cancer with Segmentation and Classification Using Fusion of Unet and Cnn Through Spider Monkey Optimization
inferred← citeshttps://doi.org/10.1007/978-3-031-73483-0_1openalex-snapshot
Assessing the Efficacy of Foundation Models in Pancreas Segmentation
inferred← citeshttps://doi.org/10.1007/s00138-024-01619-yopenalex-snapshot
Optimize multiscale feature hybrid-net deep learning approach used for automatic pancreas image segmentation
inferred← citeshttps://doi.org/10.1007/s00530-022-00963-1openalex-snapshot
FPF-Net: feature propagation and fusion based on attention mechanism for pancreas segmentation
inferred← citeshttps://doi.org/10.1007/s10346-022-01983-8openalex-snapshot
Sematic segmentation of loess landslides with STAPLE mask and fully connected conditional random field
inferred← citeshttps://doi.org/10.1007/s10462-024-11050-4openalex-snapshot
Deep learning for pancreas segmentation on computed tomography: a systematic review
inferred← citeshttps://doi.org/10.1007/s11633-021-1313-0openalex-snapshot
Supervised and Semi-supervised Methods for Abdominal Organ Segmentation: A Review
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Pancreatic cancer detection through semantic segmentation of CT images: a short review
inferred← citeshttps://doi.org/10.1016/j.bspc.2022.103519openalex-snapshot
Pancreas segmentation by two-view feature learning and multi-scale supervision
inferred← citeshttps://doi.org/10.1016/j.bspc.2024.107413openalex-snapshot
Automated detection of pancreatic cancer with segmentation and classification using fusion of UNET and CNN through spider monkey optimization
inferred← citeshttps://doi.org/10.1016/j.compbiomed.2021.104269openalex-snapshot
Domain adversarial networks and intensity-based data augmentation for male pelvic organ segmentation in cone beam CT
inferred← citeshttps://doi.org/10.1016/j.eswa.2023.121064openalex-snapshot
LMNS-Net: Lightweight Multiscale Novel Semantic-Net deep learning approach used for automatic pancreas image segmentation in CT scan images
inferred← citeshttps://doi.org/10.1016/j.imu.2024.101597openalex-snapshot
EcD-Net: Encoder-Corollary Atrous Spatial Pyramid Pooling-decoder network for automated pancreas segmentation of 2D CT images
inferred← citeshttps://doi.org/10.1016/j.patrec.2023.05.004openalex-snapshot
Incorporating multi-stage spatial visual cues and active localization offset for pancreas segmentation
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Deep Learning Algorithms for Pancreas Segmentation from Radiology Scans
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Pancreatic tumor detection in computed tomography images through a rotary positional siamese vision transformer
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A Novel Pancreatic Tumor Detection and Diagnosis Using Adaptive TransResUnet Aided Segmentation and ASPP with Multi-Scale EfficientNet-Based Classification
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MCMC Guided CNN Training and Segmentation for Pancreas Extraction
inferred← citeshttps://doi.org/10.1109/access.2023.3320570openalex-snapshot
Automatic Segmentation of Pancreas and Pancreatic Tumor: A Review of a Decade of Research
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State-of-the-Art and Challenges in Pancreatic CT Segmentation: A Systematic Review of U-Net and Its Variants
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Exploring the Impact of Data Augmentation : Traditional Methods vs. Deep Learning Approaches
inferred← citeshttps://doi.org/10.1109/concapan63470.2024.10933605openalex-snapshot
Guided Data Augmentation by Transfer Function using Uncertainty Scores for Medical Image Classification
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Automated Anomaly Detection for Surface Defects by Dual Generative Networks With Limited Training Data
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Multiplanar Data Augmentation and Lightweight Skip Connection Design for Deep-Learning-Based Abdominal CT Image Segmentation
inferred← citeshttps://doi.org/10.1111/1754-9485.13261openalex-snapshot
A review of medical image data augmentation techniques for deep learning applications
inferred← citeshttps://doi.org/10.1117/1.jmi.9.2.024002openalex-snapshot
Morphology-guided deep learning framework for segmentation of pancreas in computed tomography images
inferred← citeshttps://doi.org/10.1155/2021/1188414openalex-snapshot
Pancreatic Cancer Survival Prediction: A Survey of the State-of-the-Art
inferred← citeshttps://doi.org/10.12677/mos.2024.133213openalex-snapshot
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inferred← citeshttps://doi.org/10.1364/boe.461183openalex-snapshot
Depth-extended acoustic-resolution photoacoustic microscopy based on a two-stage deep learning network
inferred← citeshttps://doi.org/10.1371/journal.pone.0292785openalex-snapshot
CT scan pancreatic cancer segmentation and classification using deep learning and the tunicate swarm algorithm
inferred← citeshttps://doi.org/10.17341/gazimmfd.1335533openalex-snapshot
Dermoskopik görüntülerde lezyon bölütleme için iyileştirme önerileri
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Automatic pancreas segmentation using ResNet-18 deep learning approach
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Tıbbi Görüntülemede Derin Üretken Modeller : Bir Literatür Taraması
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A Deep Learning Image Data Augmentation Method for Single Tumor Segmentation
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A deep learning-based cascade algorithm for pancreatic tumor segmentation
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Smart(Sampling)Augment: Optimal and Efficient Data Augmentation for Semantic Segmentation
inferred← citeshttps://doi.org/10.3390/app112311483openalex-snapshot
Special Issue on “Machine Learning/Deep Learning in Medical Image Processing”
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Artificial Intelligence Applied to Pancreatic Imaging: A Narrative Review
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A Multi-Objective Semantic Segmentation Algorithm Based on Improved U-Net Networks
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DeepLabV3+ and Attention-Based Deep Learning Method for Pancreas Segmentation
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Mixup Veri Artırma Yöntemi ile Retina Damar Bölütlemesi
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