On the public citation graph

A Semiautomated Deep Learning Approach for Pancreas Segmentation

https://doi.org/10.1155/2021/3284493

0 signed · 65 inferred · view as graph

Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.

Is this your work? Sign in with ORCID and put your name on the claims that are yours.

Claim with ORCID

Reference health. See whether every reference in this work resolves to a known work and none has been retracted — and get a dated CiteStamped badge you can embed.

See reference health

What this work says

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

inferredcites →https://doi.org/10.1007/978-3-030-13969-8_1openalex-snapshot

Pancreas Segmentation in CT and MRI via Task-Specific Network Design and Recurrent Neural Contextual Learning

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/10.1007/978-3-319-46723-8_52openalex-snapshot

Spatial Aggregation of Holistically-Nested Networks for Automated Pancreas Segmentation

inferredcites →https://doi.org/10.1007/978-3-319-46726-9_26openalex-snapshot

Soft Multi-organ Shape Models via Generalized PCA: A General Framework

inferredcites →https://doi.org/10.1007/978-3-319-66179-7_38openalex-snapshot

BRIEFnet: Deep Pancreas Segmentation Using Binary Sparse Convolutions

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/10.1007/978-3-319-66182-7_83openalex-snapshot

Towards Image-Guided Pancreas and Biliary Endoscopy: Automatic Multi-organ Segmentation on Abdominal CT with Dense Dilated Networks

inferredcites →https://doi.org/10.1007/978-3-319-67558-9_26openalex-snapshot

3D FCN Feature Driven Regression Forest-Based Pancreas Localization and Segmentation

inferredcites →https://doi.org/10.1007/978-3-319-67558-9_28openalex-snapshot

Generalised Dice Overlap as a Deep Learning Loss Function for Highly Unbalanced Segmentations

inferredcites →https://doi.org/10.1007/s11548-018-1797-4openalex-snapshot

TernaryNet: faster deep model inference without GPUs for medical 3D segmentation using sparse and binary convolutions

inferredcites →https://doi.org/10.1016/j.cviu.2007.07.007openalex-snapshot

An object-based comparative methodology for motion detection based on the F-Measure

inferredcites →https://doi.org/10.1016/j.media.2015.11.003openalex-snapshot

Joint optimization of segmentation and shape prior from level-set-based statistical shape model, and its application to the automated segmentation of abdominal organs

inferredcites →https://doi.org/10.1016/j.media.2017.03.006openalex-snapshot

Multi-atlas pancreas segmentation: Atlas selection based on vessel structure

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/10.1080/17474124.2018.1496015openalex-snapshot

Pancreas volume in health and disease: a systematic review and meta-analysis

inferredcites →https://doi.org/10.1109/3dv.2018.00083openalex-snapshot

A 3D Coarse-to-Fine Framework for Volumetric Medical Image Segmentation

inferredcites →https://doi.org/10.1109/access.2018.2880211openalex-snapshot

DeCNT: Deep Deformable CNN for Table Detection

inferredcites →https://doi.org/10.1109/access.2019.2961125openalex-snapshot

Automatic Pancreas Segmentation via Coarse Location and Ensemble Learning

inferredcites →https://doi.org/10.1109/access.2020.2975983openalex-snapshot

HRED-Net: High-Resolution Encoder-Decoder Network for Fine-Grained Image Segmentation

inferredcites →https://doi.org/10.1109/cvpr.2015.7298965openalex-snapshot

Fully convolutional networks for semantic segmentation

inferredcites →https://doi.org/10.1109/cvpr.2018.00864openalex-snapshot

Recurrent Saliency Transformation Network: Incorporating Multi-stage Visual Cues for Small Organ Segmentation

inferredcites →https://doi.org/10.1109/iccv.2017.89openalex-snapshot

Deformable Convolutional Networks

inferredcites →https://doi.org/10.1109/isbi.2019.8759329openalex-snapshot

A Novel Focal Tversky Loss Function With Improved Attention U-Net for Lesion Segmentation

inferredcites →https://doi.org/10.1109/jbhi.2020.3023462openalex-snapshot

Automatic Pancreas Segmentation in CT Images With Distance-Based Saliency-Aware DenseASPP Network

inferredcites →https://doi.org/10.1109/tip.2016.2624198openalex-snapshot

A Bottom-Up Approach for Pancreas Segmentation Using Cascaded Superpixels and (Deep) Image Patch Labeling

inferredcites →https://doi.org/10.1109/tip.2019.2902784openalex-snapshot

Deformable Object Tracking With Gated Fusion

inferredcites →https://doi.org/10.1109/tip.2020.3042059openalex-snapshot

Light Field Image Super-Resolution Using Deformable Convolution

inferredcites →https://doi.org/10.1109/tmi.2013.2265805openalex-snapshot

Automated Abdominal Multi-Organ Segmentation With Subject-Specific Atlas Generation

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.2019.2911588openalex-snapshot

Deep Q Learning Driven CT Pancreas Segmentation With Geometry-Aware U-Net

inferredcites →https://doi.org/10.1109/tpami.2016.2572683openalex-snapshot

Fully Convolutional Networks for Semantic Segmentation

inferredcites →https://doi.org/10.1109/tpami.2017.2699184openalex-snapshot

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

inferredcites →https://doi.org/10.1109/tpami.2018.2858826openalex-snapshot

Focal Loss for Dense Object Detection

inferredcites →https://doi.org/10.1136/gut.2006.103333openalex-snapshot

Biology and management of pancreatic cancer

inferredcites →https://doi.org/10.1145/3065386openalex-snapshot

ImageNet classification with deep convolutional neural networks

inferredcites →https://doi.org/10.1145/3364836.3364894openalex-snapshot

Fixed-Point Deformable U-Net for Pancreas CT Segmentation

inferredcites →https://doi.org/10.2307/1932409openalex-snapshot

Measures of the Amount of Ecologic Association Between Species

inferredcites →https://doi.org/10.48550/arxiv.1412.7062openalex-snapshot

Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs

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.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.

inferred← citeshttps://doi.org/10.2139/ssrn.4892872openalex-extracted

Automated Detection of Pancreatic Cancer with Segmentation and Classification Using Fusion of Unet and Cnn Through Spider Monkey Optimization

inferred← citeshttps://doi.org/10.1002/appl.70021openalex-snapshot

Wavelet‐Enhanced Deformable Convolutional Network for Breast Cancer Classification in High‐Resolution Histopathology Images

inferred← citeshttps://doi.org/10.1002/ima.70025openalex-snapshot

Pancreas Segmentation Based on Multi‐Stage Attention Enhanced U‐Net

inferred← citeshttps://doi.org/10.1002/ima.70064openalex-snapshot

TS ‐Net: Trans‐Scale Network for Medical Image Segmentation

inferred← citeshttps://doi.org/10.1002/ueg2.12723openalex-snapshot

Artificial Intelligence in Pancreatic Imaging: A Systematic Review

inferred← citeshttps://doi.org/10.1007/978-3-031-47686-0_28openalex-snapshot

An Efficient Approach for Pancreas Segmentation in Computer Tomography Scans

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/s10489-024-05780-9openalex-snapshot

MDHT-Net: Multi-scale Deformable U-Net with Cos-spatial and Channel Hybrid Transformer for pancreas segmentation

inferred← citeshttps://doi.org/10.1007/s11760-025-04923-wopenalex-snapshot

TriA-TransUNet: Triple attention-driven with morphological adaptation network for pancreas segmentation in CT images

inferred← citeshttps://doi.org/10.1007/s44163-024-00148-xopenalex-snapshot

Pancreatic cancer detection through semantic segmentation of CT images: a short review

inferred← citeshttps://doi.org/10.1016/j.bbadva.2023.100084openalex-snapshot

Automated Image Analysis Method to Detect and Quantify Fat Cell Infiltration in Hematoxylin and Eosin Stained Human Pancreas Histology Images

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.compmedimag.2024.102434openalex-snapshot

Main challenges on the curation of large scale datasets for pancreas segmentation using deep learning in multi-phase CT scans: Focus on cardinality, manual refinement, and annotation quality

inferred← citeshttps://doi.org/10.1016/j.neucom.2022.10.060openalex-snapshot

TD-Net: Trans-Deformer network for automatic pancreas segmentation

inferred← citeshttps://doi.org/10.1016/j.neunet.2022.10.026openalex-snapshot

Temperature guided network for 3D joint segmentation of the pancreas and tumors

inferred← citeshttps://doi.org/10.1016/j.neunet.2025.107414openalex-snapshot

LUNETR: Language-Infused UNETR for precise pancreatic tumor segmentation in 3D medical image

inferred← citeshttps://doi.org/10.1109/access.2024.3392595openalex-snapshot

State-of-the-Art and Challenges in Pancreatic CT Segmentation: A Systematic Review of U-Net and Its Variants

inferred← citeshttps://doi.org/10.1109/dicta60407.2023.00050openalex-snapshot

Attention and Pooling based Sigmoid Colon Segmentation in 3D CT images

inferred← citeshttps://doi.org/10.1109/icstsn61422.2024.10671042openalex-snapshot

U-Net based Pancreas Segmentation from Computed Tomography Images

inferred← citeshttps://doi.org/10.1109/ictbig68706.2025.11323741openalex-snapshot

Hybrid Framework for Liver Image Enhancement and Detection Using Double-Stage Gaussian Filter and CNN

inferred← citeshttps://doi.org/10.1177/08953996251313719openalex-snapshot

A deep learning detection method for pancreatic cystic neoplasm based on Mamba architecture

inferred← citeshttps://doi.org/10.3389/fpubh.2021.797877openalex-snapshot

A Novel Extra Tree Ensemble Optimized DL Framework (ETEODL) for Early Detection of Diabetes

inferred← citeshttps://doi.org/10.3390/cancers16132403openalex-snapshot

Improved Pancreatic Cancer Detection and Localization on CT Scans: A Computer-Aided Detection Model Utilizing Secondary Features

Editorial review for independent work. ICSAC — the Institute for Complexity Science and Advanced Computing — runs a structured editorial panel for researchers publishing outside journals, and publishes on CiteStamp rails. Submission is free and goes through ORCID: icsacinstitute.org/submit.