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LeVit-UNet: Make Faster Encoders with Transformer for Biomedical Image Segmentation

https://doi.org/10.2139/ssrn.4116174
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8/8 checkable references clean · checked 2026-08-28

Every reference with a DOI in the deposited reference list resolved to a known work in Crossref or DataCite at the dated check, and none carried a retraction, withdrawal, or removal notice.

24 without a DOI — not checked. A reference deposited without a DOI is never matched by title or guessed at; it stays outside the checked set, and this line discloses that.

The 8 checked references that resolve
resolves10.1109/TPAMI.2016.2644615
SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
resolves10.1109/TPAMI.2017.2699184
DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs
resolves10.1007/978-3-030-59719-1_11
Learning Directional Feature Maps for Cardiac MRI Segmentation
resolves10.1109/TMI.2018.2845918
H-DenseUNet: Hybrid Densely Connected UNet for Liver and Tumor Segmentation From CT Volumes
resolves10.1109/TMI.2019.2959609
UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentation
resolves10.1038/s41592-020-01008-z
nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation
resolves10.1109/3DV.2016.79
V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation
resolves10.1126/science.1191776
Micro-Optical Sectioning Tomography to Obtain a High-Resolution Atlas of the Mouse Brain
The 24 references without a DOI — listed, not checked
no DOI — not checkedFully convolutional networks for semantic segmentation
no DOI — not checkedU-Net: Convolutional Networks for Biomedical Image Segmentation
no DOI — not checkedCcnet: Criss-cross attention for semantic segmentation
no DOI — not checkedRA-UNet: A hybrid deep attention-aware network to extract liver and tumor in CT scans
no DOI — not checkedref8
no DOI — not checkedPyramid scene parsing network
no DOI — not checkedref10
no DOI — not checkedSuper-BPD: Super boundary-to-pixel direction for fast image segmentation
no DOI — not checkedDeep high-resolution representation learning for human pose estimation
no DOI — not checkedHigherhrnet: Scale-aware representation learning for bottom-up human pose estimation
no DOI — not checkedref14
no DOI — not checkedref15
no DOI — not checkedref16
no DOI — not checkedref17
no DOI — not checkedTraining data-efficient image transformers & distillation through attention
no DOI — not checkedLeViT: a Vision Transformer in ConvNet's Clothing for Faster Inference
no DOI — not checkedRethinking semantic segmentation from a sequence-tosequence perspective with transformers
no DOI — not checkedSwin transformer: Hierarchical vision transformer using shifted windows
no DOI — not checkedref22
no DOI — not checkedref23
no DOI — not checkedWeighted Res-UNet for High-Quality Retina Vessel Segmentation
no DOI — not checked3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation
no DOI — not checkedSqueeze-and-excitation networks
no DOI — not checkedDual attention network for scene segmentation
What this badge says. CiteStamped means the CHECKABLE references of this work were clean at the dated check: each resolved to a known work in a public registry, and none carried a retraction notice at that time. It says nothing about the quality, findings, or importance of the work itself, and nothing about references deposited without a DOI.

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