On the public citation graph
Detection and Quantification of Opacity in Vitreous Ultrasound Images Based on Mdp-Net
0 signed · 17 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.
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known work and none has been retracted — and get a dated CiteStamped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.
What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1002/mp.14470openalex-extracted
An attention‐supervised full‐resolution residual network for the segmentation of breast ultrasound images
inferredcites →https://doi.org/10.1007/s00347-020-01094-6openalex-extracted
Degenerative Glaskörpertrübungen
inferredcites →https://doi.org/10.1007/s11042-018-6032-3openalex-extracted
A novel method for automatic retinal detachment detection and estimation using ocular ultrasound image
inferredcites →https://doi.org/10.1007/s11042-022-13183-6openalex-extracted
Computer-aided diagnosis of COVID-19 from chest X-ray images using histogram-oriented gradient features and Random Forest classifier
inferredcites →https://doi.org/10.1007/s13755-022-00204-9openalex-extracted
MDU-Net: multi-scale densely connected U-Net for biomedical image segmentation
inferredcites →https://doi.org/10.1016/j.bspc.2020.102027openalex-extracted
Breast mass segmentation in ultrasound with selective kernel U-Net convolutional neural network
inferredcites →https://doi.org/10.1016/j.bspc.2022.104249openalex-extracted
Multi-task thyroid tumor segmentation based on the joint loss function
inferredcites →https://doi.org/10.1016/j.bspc.2023.104584openalex-extracted
Deep learning segmentation, classification, and risk prediction of complex vascular lesions on intravascular ultrasound images
inferredcites →https://doi.org/10.1016/j.comcom.2020.01.013openalex-extracted
Cascading handcrafted features and Convolutional Neural Network for IoT-enabled brain tumor segmentation
inferredcites →https://doi.org/10.1016/j.eswa.2022.119024openalex-extracted
CSwin-PNet: A CNN-Swin Transformer combined pyramid network for breast lesion segmentation in ultrasound images
inferredcites →https://doi.org/10.1016/j.neuroimage.2019.03.068openalex-extracted
Multi-branch convolutional neural network for multiple sclerosis lesion segmentation
inferredcites →https://doi.org/10.1109/access.2019.2914873openalex-extracted
Attention Dense-U-Net for Automatic Breast Mass Segmentation in Digital Mammogram
inferredcites →https://doi.org/10.1109/access.2021.3058522openalex-extracted
PA-MVSNet: Sparse-to-Dense Multi-View Stereo With Pyramid Attention
inferredcites →https://doi.org/10.1109/tmi.2018.2845918openalex-extracted
H-DenseUNet: Hybrid Densely Connected UNet for Liver and Tumor Segmentation From CT Volumes
inferredcites →https://doi.org/10.2147/opth.s229857openalex-extracted
A Proposed Method to Quantify Vitreous Hemorrhage by Ultrasound
inferredcites →https://doi.org/10.3389/fnagi.2022.948704openalex-extracted
CNNG: A Convolutional Neural Networks With Gated Recurrent Units for Autism Spectrum Disorder Classification
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.