EPRA U-Net: An Efficient Pyramid Residual Attention Framework for Accurate Infarct Segmentation in Diffusion-Weighted MRI | Paper Citation Record

Observation baf71640-8f46-4814-b50f-ddac8340c25d · outbound

This paper cites Current concepts on magnetic resonance imaging (MRI) perfusion- diffusion assessment in acute ischaemic stroke: a review & an update for the clinicians.,.

EPRA U-Net: An Efficient Pyramid Residual Attention Framework for Accurate Infarct Segmentation in Diffusion-Weighted MRI Current concepts on magnetic resonance imaging (MRI) perfusion- diffusion assessment in acute ischaemic stroke: a review & an update for the clinicians.,

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This paper cites World Stroke Organization: Global Stroke Fact Sheet 2025,.

EPRA U-Net: An Efficient Pyramid Residual Attention Framework for Accurate Infarct Segmentation in Diffusion-Weighted MRI World Stroke Organization: Global Stroke Fact Sheet 2025,

Reference 2

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This paper cites TeleStroke: real -time stroke detection with federated learning and YOLOv8 on edge devices,.

EPRA U-Net: An Efficient Pyramid Residual Attention Framework for Accurate Infarct Segmentation in Diffusion-Weighted MRI TeleStroke: real -time stroke detection with federated learning and YOLOv8 on edge devices,

Reference 3

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This paper cites Diffusion weighted imaging in acute ischemic stroke: A review of its interpretation pitfalls and advanced diffusion imaging application,.

EPRA U-Net: An Efficient Pyramid Residual Attention Framework for Accurate Infarct Segmentation in Diffusion-Weighted MRI Diffusion weighted imaging in acute ischemic stroke: A review of its interpretation pitfalls and advanced diffusion imaging application,

Reference 4

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This paper cites Artificial Intelligence in Stroke Care: A Narrative Review of Diagnostic, Predictive, and Workflow Applications,.

EPRA U-Net: An Efficient Pyramid Residual Attention Framework for Accurate Infarct Segmentation in Diffusion-Weighted MRI Artificial Intelligence in Stroke Care: A Narrative Review of Diagnostic, Predictive, and Workflow Applications,

Reference 5

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Observation a79f32b3-a441-45c2-a4cf-54abad4d2df7 · outbound

This paper cites U -Net: Convolutional Networks for Biomedical Image Segmentation,.

EPRA U-Net: An Efficient Pyramid Residual Attention Framework for Accurate Infarct Segmentation in Diffusion-Weighted MRI U -Net: Convolutional Networks for Biomedical Image Segmentation,

Reference 6

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This paper cites UNet++: A Nested U-Net Architecture for Medical Image Segmentation,.

EPRA U-Net: An Efficient Pyramid Residual Attention Framework for Accurate Infarct Segmentation in Diffusion-Weighted MRI UNet++: A Nested U-Net Architecture for Medical Image Segmentation,

Reference 7

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This paper cites Attention U-Net: Learning Where to Look for the Pancreas.

EPRA U-Net: An Efficient Pyramid Residual Attention Framework for Accurate Infarct Segmentation in Diffusion-Weighted MRI Attention U-Net: Learning Where to Look for the Pancreas

Reference 8

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

EPRA U-Net: An Efficient Pyramid Residual Attention Framework for Accurate Infarct Segmentation in Diffusion-Weighted MRI Recurrent Residual Convolutional Neural Network based on U-Net (R2U-Net) for Medical Image Segmentation

Reference 9

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This paper cites TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation.

EPRA U-Net: An Efficient Pyramid Residual Attention Framework for Accurate Infarct Segmentation in Diffusion-Weighted MRI TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation

Reference 10

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This paper cites Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentation.

EPRA U-Net: An Efficient Pyramid Residual Attention Framework for Accurate Infarct Segmentation in Diffusion-Weighted MRI Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentation

Reference 11

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This paper cites Medical Image Segmentation Review: The Success of U-Net,.

EPRA U-Net: An Efficient Pyramid Residual Attention Framework for Accurate Infarct Segmentation in Diffusion-Weighted MRI Medical Image Segmentation Review: The Success of U-Net,

Reference 12

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This paper cites 3D U -Net: Learning Dense Volumetric Segmentation from Sparse Annotation,.

EPRA U-Net: An Efficient Pyramid Residual Attention Framework for Accurate Infarct Segmentation in Diffusion-Weighted MRI 3D U -Net: Learning Dense Volumetric Segmentation from Sparse Annotation,

Reference 13

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This paper cites V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation,.

EPRA U-Net: An Efficient Pyramid Residual Attention Framework for Accurate Infarct Segmentation in Diffusion-Weighted MRI V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation,

Reference 14

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This paper cites Efficient multi -scale 3D CNN with fully connected CRF for accurate brain lesion segmentation,.

EPRA U-Net: An Efficient Pyramid Residual Attention Framework for Accurate Infarct Segmentation in Diffusion-Weighted MRI Efficient multi -scale 3D CNN with fully connected CRF for accurate brain lesion segmentation,

Reference 15

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This paper cites nnU-Net: a self -configuring method for deep learning -based biomedical image segmentation,.

EPRA U-Net: An Efficient Pyramid Residual Attention Framework for Accurate Infarct Segmentation in Diffusion-Weighted MRI nnU-Net: a self -configuring method for deep learning -based biomedical image segmentation,

Reference 16

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This paper cites Encoder -Decoder with Atrous Separable Convolution for Semantic Image Segmentation,.

EPRA U-Net: An Efficient Pyramid Residual Attention Framework for Accurate Infarct Segmentation in Diffusion-Weighted MRI Encoder -Decoder with Atrous Separable Convolution for Semantic Image Segmentation,

Reference 17

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This paper cites Pyramid Scene Parsing Network,.

EPRA U-Net: An Efficient Pyramid Residual Attention Framework for Accurate Infarct Segmentation in Diffusion-Weighted MRI Pyramid Scene Parsing Network,

Reference 18

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This paper cites Attention is All you Need,.

EPRA U-Net: An Efficient Pyramid Residual Attention Framework for Accurate Infarct Segmentation in Diffusion-Weighted MRI Attention is All you Need,

Reference 19

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This paper cites CBAM: Convolutional Block Attention Module,.

EPRA U-Net: An Efficient Pyramid Residual Attention Framework for Accurate Infarct Segmentation in Diffusion-Weighted MRI CBAM: Convolutional Block Attention Module,

Reference 20

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EPRA U-Net: An Efficient Pyramid Residual Attention Framework for Accurate Infarct Segmentation in Diffusion-Weighted MRI Dual Attention Network for Scene Segmentation,

Reference 21

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This paper cites Generalised Dice Overlap as a Deep Learning Loss Function for Highly Unbalanced Segmentations,.

EPRA U-Net: An Efficient Pyramid Residual Attention Framework for Accurate Infarct Segmentation in Diffusion-Weighted MRI Generalised Dice Overlap as a Deep Learning Loss Function for Highly Unbalanced Segmentations,

Reference 22

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This paper cites Tackling the class imbalance problem of deep learning-based head and neck organ segmentation,.

EPRA U-Net: An Efficient Pyramid Residual Attention Framework for Accurate Infarct Segmentation in Diffusion-Weighted MRI Tackling the class imbalance problem of deep learning-based head and neck organ segmentation,

Reference 23

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This paper cites Tversky Loss Function for Image Segmentation Using 3D Fully Convolutional Deep Networks,.

EPRA U-Net: An Efficient Pyramid Residual Attention Framework for Accurate Infarct Segmentation in Diffusion-Weighted MRI Tversky Loss Function for Image Segmentation Using 3D Fully Convolutional Deep Networks,

Reference 24

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This paper cites A comprehensive survey of loss functions and metrics in deep learning,.

EPRA U-Net: An Efficient Pyramid Residual Attention Framework for Accurate Infarct Segmentation in Diffusion-Weighted MRI A comprehensive survey of loss functions and metrics in deep learning,

Reference 25

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This paper cites A Novel Focal Tversky Loss Function With Improved Attention U-Net for Lesion Segmentation,.

EPRA U-Net: An Efficient Pyramid Residual Attention Framework for Accurate Infarct Segmentation in Diffusion-Weighted MRI A Novel Focal Tversky Loss Function With Improved Attention U-Net for Lesion Segmentation,

Reference 26

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This paper cites The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS),.

EPRA U-Net: An Efficient Pyramid Residual Attention Framework for Accurate Infarct Segmentation in Diffusion-Weighted MRI The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS),

Reference 27

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This paper cites Deep Learning-Driven Segmentation of Ischemic Stroke Lesions Using Multi-Channel MRI.

EPRA U-Net: An Efficient Pyramid Residual Attention Framework for Accurate Infarct Segmentation in Diffusion-Weighted MRI Deep Learning-Driven Segmentation of Ischemic Stroke Lesions Using Multi-Channel MRI

Reference 28

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This paper cites Random effects during training: Implications for deep learning -based medical image segmentation,.

EPRA U-Net: An Efficient Pyramid Residual Attention Framework for Accurate Infarct Segmentation in Diffusion-Weighted MRI Random effects during training: Implications for deep learning -based medical image segmentation,

Reference 29

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This paper cites Robust chest CT image segmentation of COVID -19 lung infection based on limited data,.

EPRA U-Net: An Efficient Pyramid Residual Attention Framework for Accurate Infarct Segmentation in Diffusion-Weighted MRI Robust chest CT image segmentation of COVID -19 lung infection based on limited data,

Reference 30

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This paper cites Detection, Diagnosis and Treatment of Acute Ischemic Stroke: Current and Future Perspectives,.

EPRA U-Net: An Efficient Pyramid Residual Attention Framework for Accurate Infarct Segmentation in Diffusion-Weighted MRI Detection, Diagnosis and Treatment of Acute Ischemic Stroke: Current and Future Perspectives,

Reference 31

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This paper cites Make Sense: Free to use online annotation tool,.

EPRA U-Net: An Efficient Pyramid Residual Attention Framework for Accurate Infarct Segmentation in Diffusion-Weighted MRI Make Sense: Free to use online annotation tool,

Reference 32

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EPRA U-Net: An Efficient Pyramid Residual Attention Framework for Accurate Infarct Segmentation in Diffusion-Weighted MRI Effect of data leakage in brain MRI classification using 2D convolutional neural networks,

Reference 33

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This paper cites A Flexible 2.5D Medical Image Segmentation Approach with In-Slice and Cross-Slice Attention.

EPRA U-Net: An Efficient Pyramid Residual Attention Framework for Accurate Infarct Segmentation in Diffusion-Weighted MRI A Flexible 2.5D Medical Image Segmentation Approach with In-Slice and Cross-Slice Attention

Reference 34

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EPRA U-Net: An Efficient Pyramid Residual Attention Framework for Accurate Infarct Segmentation in Diffusion-Weighted MRI EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

Reference 35

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EPRA U-Net: An Efficient Pyramid Residual Attention Framework for Accurate Infarct Segmentation in Diffusion-Weighted MRI Deep Residual Learning for Image Recognition,

Reference 36

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This paper cites DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs,.

EPRA U-Net: An Efficient Pyramid Residual Attention Framework for Accurate Infarct Segmentation in Diffusion-Weighted MRI DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs,

Reference 37

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This paper cites Densely Connected Recurrent Residual (Dense R2UNet) Convolutional Neural Network for Segmentation of Lung CT Images.

EPRA U-Net: An Efficient Pyramid Residual Attention Framework for Accurate Infarct Segmentation in Diffusion-Weighted MRI Densely Connected Recurrent Residual (Dense R2UNet) Convolutional Neural Network for Segmentation of Lung CT Images

Reference 38

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This paper cites A semi -automatic segmentation method for meningioma developed using a variational approach model,.

EPRA U-Net: An Efficient Pyramid Residual Attention Framework for Accurate Infarct Segmentation in Diffusion-Weighted MRI A semi -automatic segmentation method for meningioma developed using a variational approach model,

Reference 39

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This paper cites Image Segmentation Evaluation With the Dice Index: Methodological Issues,.

EPRA U-Net: An Efficient Pyramid Residual Attention Framework for Accurate Infarct Segmentation in Diffusion-Weighted MRI Image Segmentation Evaluation With the Dice Index: Methodological Issues,

Reference 40

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This paper cites Towards a guideline for evaluation metrics in medical image segmentation,.

EPRA U-Net: An Efficient Pyramid Residual Attention Framework for Accurate Infarct Segmentation in Diffusion-Weighted MRI Towards a guideline for evaluation metrics in medical image segmentation,

Reference 41

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This paper cites Common Limitations of Image Processing Metrics: A Picture Story.

EPRA U-Net: An Efficient Pyramid Residual Attention Framework for Accurate Infarct Segmentation in Diffusion-Weighted MRI Common Limitations of Image Processing Metrics: A Picture Story

Reference 42

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This paper cites Detection and classification of COVID-19 by using faster R-CNN and mask R-CNN on CT images,.

EPRA U-Net: An Efficient Pyramid Residual Attention Framework for Accurate Infarct Segmentation in Diffusion-Weighted MRI Detection and classification of COVID-19 by using faster R-CNN and mask R-CNN on CT images,

Reference 43

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This paper cites U -Net and Its Variants for Medical Image Segmentation: A Review of Theory and Applications,.

EPRA U-Net: An Efficient Pyramid Residual Attention Framework for Accurate Infarct Segmentation in Diffusion-Weighted MRI U -Net and Its Variants for Medical Image Segmentation: A Review of Theory and Applications,

Reference 44

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This paper cites Deep learning -based approach for detecting COVID -19 in chest X -rays,.

EPRA U-Net: An Efficient Pyramid Residual Attention Framework for Accurate Infarct Segmentation in Diffusion-Weighted MRI Deep learning -based approach for detecting COVID -19 in chest X -rays,

Reference 45

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This paper cites Calibrating the Dice Loss to Handle Neural Network Overconfidence for Biomedical Image Segmentation,.

EPRA U-Net: An Efficient Pyramid Residual Attention Framework for Accurate Infarct Segmentation in Diffusion-Weighted MRI Calibrating the Dice Loss to Handle Neural Network Overconfidence for Biomedical Image Segmentation,

Reference 46

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This paper cites Retinoblastoma Detection via Image Processing and Interpretable Artificial Intelligence Techniques,.

EPRA U-Net: An Efficient Pyramid Residual Attention Framework for Accurate Infarct Segmentation in Diffusion-Weighted MRI Retinoblastoma Detection via Image Processing and Interpretable Artificial Intelligence Techniques,

Reference 47

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This paper cites From coarse to fine: a deep 3D probability volume contours framework for tumour segmentation and dose painting in PET images,.

EPRA U-Net: An Efficient Pyramid Residual Attention Framework for Accurate Infarct Segmentation in Diffusion-Weighted MRI From coarse to fine: a deep 3D probability volume contours framework for tumour segmentation and dose painting in PET images,

Reference 48

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This paper cites Individual Comparisons by Ranking Methods,.

EPRA U-Net: An Efficient Pyramid Residual Attention Framework for Accurate Infarct Segmentation in Diffusion-Weighted MRI Individual Comparisons by Ranking Methods,

Reference 49

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This paper cites Parametric versus non -parametric statistics in the analysis of randomized trials with non - normally distributed data,.

EPRA U-Net: An Efficient Pyramid Residual Attention Framework for Accurate Infarct Segmentation in Diffusion-Weighted MRI Parametric versus non -parametric statistics in the analysis of randomized trials with non - normally distributed data,

Reference 50

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Observation 36e9b972-a989-434d-8a82-51840ceb6eb4 · outbound

This paper cites Note on the Sampling Error of the Difference Between Correlated Proportions or Percentages,.

EPRA U-Net: An Efficient Pyramid Residual Attention Framework for Accurate Infarct Segmentation in Diffusion-Weighted MRI Note on the Sampling Error of the Difference Between Correlated Proportions or Percentages,

Reference 51

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This paper cites Efron and R.

EPRA U-Net: An Efficient Pyramid Residual Attention Framework for Accurate Infarct Segmentation in Diffusion-Weighted MRI Efron and R

Reference 52

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Observation e9c108aa-b48b-4ac6-a215-88fc57acc93e · outbound

This paper cites Cohen, Statistical Power Analysis for the Behavioral Sciences, 2nd ed.

EPRA U-Net: An Efficient Pyramid Residual Attention Framework for Accurate Infarct Segmentation in Diffusion-Weighted MRI Cohen, Statistical Power Analysis for the Behavioral Sciences, 2nd ed

Reference 53

Resolution: unresolved; no resolver link, observed 2026-07-12T01:30:31.511284Z

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Observation 3378a79a-79e2-4a05-9c6f-db0a9b337b25 · outbound

This paper cites Resolving power: a general approach to compare the distinguishing ability of threshold -free evaluation metrics,.

EPRA U-Net: An Efficient Pyramid Residual Attention Framework for Accurate Infarct Segmentation in Diffusion-Weighted MRI Resolving power: a general approach to compare the distinguishing ability of threshold -free evaluation metrics,

Reference 54

Resolution: verified exact; doi, observed 2026-07-12T01:38:25.421521Z

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Observation 660fa302-ede9-4a6f-ad91-2f97ce9f08f5 · outbound

This paper cites Risk-based Evaluation of ML Classification Methods Used for Medical Devices,.

EPRA U-Net: An Efficient Pyramid Residual Attention Framework for Accurate Infarct Segmentation in Diffusion-Weighted MRI Risk-based Evaluation of ML Classification Methods Used for Medical Devices,

Reference 55

Resolution: unresolved; no resolver link, observed 2026-07-12T01:30:31.511284Z

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Observation ba1f3dfe-6c3b-4779-899d-b39f5717839c · outbound

This paper cites Challenges in Deep Learning-Based Small Organ Segmentation: A Benchmarking Perspective for Medical Research with Limited Datasets.

EPRA U-Net: An Efficient Pyramid Residual Attention Framework for Accurate Infarct Segmentation in Diffusion-Weighted MRI Challenges in Deep Learning-Based Small Organ Segmentation: A Benchmarking Perspective for Medical Research with Limited Datasets

Reference 56

Resolution: verified exact; local_arxiv, observed 2026-07-12T01:38:25.404947Z

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Observation 3741f2a6-6462-4fe1-b8c5-bbadcaf0986c · outbound

This paper cites Accuracy of CT perfusion ischemic core volume and location estimation: A comparison between four ischemic core estimation approaches using syngo.via,.

EPRA U-Net: An Efficient Pyramid Residual Attention Framework for Accurate Infarct Segmentation in Diffusion-Weighted MRI Accuracy of CT perfusion ischemic core volume and location estimation: A comparison between four ischemic core estimation approaches using syngo.via,

Reference 57

Resolution: verified exact; doi, observed 2026-07-12T01:38:25.390157Z

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Observation add35aec-e865-44fa-80b4-3e86d5527689 · outbound

This paper cites Automated identification of thrombectomy amenable vessel occlusion on computed tomography angiography using deep learning,.

EPRA U-Net: An Efficient Pyramid Residual Attention Framework for Accurate Infarct Segmentation in Diffusion-Weighted MRI Automated identification of thrombectomy amenable vessel occlusion on computed tomography angiography using deep learning,

Reference 58

Resolution: verified exact; arxiv_id, observed 2026-07-12T01:38:25.385306Z

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Observation b675a4da-1ac2-490e-9e03-6d02dd3c6fb3 · outbound

This paper cites Early detection of white matter hyperintensities using SHIVA‐WMH detector,.

EPRA U-Net: An Efficient Pyramid Residual Attention Framework for Accurate Infarct Segmentation in Diffusion-Weighted MRI Early detection of white matter hyperintensities using SHIVA‐WMH detector,

Reference 59

Resolution: verified exact; doi, observed 2026-07-12T01:38:25.394684Z

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Observation 6aee1bbc-1b1b-4b23-9e92-3130c763c7c3 · outbound

This paper cites Automatic Liver Segmentation from Multiphase CT Using Modified SegNet and ASPP Module,.

EPRA U-Net: An Efficient Pyramid Residual Attention Framework for Accurate Infarct Segmentation in Diffusion-Weighted MRI Automatic Liver Segmentation from Multiphase CT Using Modified SegNet and ASPP Module,

Reference 60

Resolution: verified exact; doi, observed 2026-07-12T01:38:25.398882Z

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Observation 7f99488e-25de-422c-a959-337c69a82771 · outbound

This paper cites Rapid risk stratification of acute ischemic stroke patients in the emergency department: the incremental prognostic role of left atrial reservoir strain.

EPRA U-Net: An Efficient Pyramid Residual Attention Framework for Accurate Infarct Segmentation in Diffusion-Weighted MRI Rapid risk stratification of acute ischemic stroke patients in the emergency department: the incremental prognostic role of left atrial reservoir strain

Reference 61

Resolution: unresolved; no resolver link, observed 2026-07-12T01:30:31.511284Z

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Observation 0ab856de-fd0d-4cb4-aa15-e307cc746020 · outbound

This paper cites Weak Edge Target Segmentation Network Based on Dual Attention Mechanism,.

EPRA U-Net: An Efficient Pyramid Residual Attention Framework for Accurate Infarct Segmentation in Diffusion-Weighted MRI Weak Edge Target Segmentation Network Based on Dual Attention Mechanism,

Reference 62

Resolution: verified exact; doi, observed 2026-07-12T01:38:25.378992Z

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