References¶
This page lists the primary papers tracked in data/architectures.yml. Use the
original papers for formal citation details.
Not listed means this book does not track a verified publisher,
proceedings, or journal DOI for that paper version.
| Architecture | Paper | DOI | arXiv |
|---|---|---|---|
| Fully Convolutional Network (FCN) | Fully Convolutional Networks for Semantic Segmentation | 10.1109/CVPR.2015.7298965 | 1411.4038 |
| U-Net | U-Net: Convolutional Networks for Biomedical Image Segmentation | 10.1007/978-3-319-24574-4_28 | 1505.04597 |
| DeepLabv3+ | Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation | 10.1007/978-3-030-01234-2_49 | 1802.02611 |
| 3D U-Net | 3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation | 10.1007/978-3-319-46723-8_49 | 1606.06650 |
| V-Net | Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation | 10.1109/3DV.2016.79 | 1606.04797 |
| Residual U-Net / ResUNet-style variants | The Importance of Skip Connections in Biomedical Image Segmentation | Not listed | 1608.04117 |
| R2U-Net | Recurrent Residual Convolutional Neural Network based on U-Net (R2U-Net) for Medical Image Segmentation | Not listed | 1802.06955 |
| MultiResUNet | MultiResUNet: Rethinking the U-Net Architecture for Multimodal Biomedical Image Segmentation | 10.1016/j.neunet.2019.08.025 | 1902.04049 |
| U-Net++ | UNet++: A Nested U-Net Architecture for Medical Image Segmentation | 10.1007/978-3-030-00889-5_1 | 1807.10165 |
| UNet 3+ | UNet 3+: A Full-Scale Connected UNet for Medical Image Segmentation | 10.1109/ICASSP40776.2020.9053405 | 2004.08790 |
| Attention U-Net | Attention U-Net: Learning Where to Look for the Pancreas | Not listed | 1804.03999 |
| U²-Net | U2-Net: Going Deeper with Nested U-Structure for Salient Object Detection | 10.1016/j.patcog.2020.107404 | 2005.09007 |
| nnU-Net | nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation | 10.1038/s41592-020-01008-z | 1809.10486 |
| SegResNet | 3D MRI Brain Tumor Segmentation Using Autoencoder Regularization | 10.1007/978-3-030-11726-9_28 | 1810.11654 |
| TransUNet | TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation | Not listed | 2102.04306 |
| Swin-Unet | Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentation | 10.1007/978-3-031-25066-8_9 | 2105.05537 |
| UNETR | UNETR: Transformers for 3D Medical Image Segmentation | 10.1109/WACV51458.2022.00181 | 2103.10504 |
| Swin UNETR | Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images | 10.1007/978-3-031-08999-2_22 | 2201.01266 |
| StarDist-3D | Star-convex Polyhedra for 3D Object Detection and Segmentation in Microscopy | 10.1109/WACV45572.2020.9093435 | 1908.03636 |
| Cellpose | Cellpose: a generalist algorithm for cellular segmentation | 10.1038/s41592-020-01018-x | Not listed |
| WNet3D | CellSeg3D: self-supervised 3D cell segmentation for fluorescence microscopy | 10.7554/eLife.99848 | Not listed |
| MedSAM | Segment Anything in Medical Images | 10.1038/s41467-024-44824-z | 2304.12306 |
| SAM-Med2D | SAM-Med2D | Not listed | 2308.16184 |
| SAM-Med3D | SAM-Med3D: Towards General-purpose Segmentation Models for Volumetric Medical Images | Not listed | 2310.15161 |
| SegVol | SegVol: Universal and Interactive Volumetric Medical Image Segmentation | 10.48550/arXiv.2311.13385 | 2311.13385 |
| MedSAM2 | MedSAM2: Segment Anything in 3D Medical Images and Videos | Not listed | 2504.03600 |