{"url":"/method/uctransnet","slug":"uctransnet","name":"UCTransNet","full_name":"UCTransNet","full_name_withheld":false,"description_markdown":"**UCTransNet** is an end-to-end deep learning network for semantic segmentation that takes [U-Net](https://paperswithcode.com/method/u-net) as the main structure of the network. The original skip connections of U-Net are replaced by CTrans consisting of two components: [Channel-wise Cross fusion Transformer](https://paperswithcode.com/method/channel-wise-cross-fusion-transformer) ([CCT](https://paperswithcode.com/method/cct)) and [Channel-wise Cross Attention](https://paperswithcode.com/method/channel-wise-cross-attention) (CCA) to guide the fused multi-Scale channel-wise information to effectively connect to the decoder features for eliminating the ambiguity.","description_state":"present","introduced_year":null,"introduced_by":{"title":null,"paper":null,"first_author":null,"n_authors":0,"url_abs":null,"archive_paper_url":null},"source":{"url":"https://arxiv.org/abs/2109.04335v3","title":"UCTransNet: Rethinking the Skip Connections in U-Net from a Channel-wise Perspective with Transformer","url_on_a_paper_host":true},"code_snippet_url":null,"code_snippet_url_on_a_code_host":false,"categories":[{"area":"Computer Vision","area_id":"computer-vision","collection":"Semantic Segmentation Models","url":"/methods/category/semantic-segmentation-models","pwc_aliases":["segmentation-models"]}],"n_papers_tagged":4,"archive_num_papers":null,"papers_newest_first":[{"paper":"/paper/boosting-medical-image-segmentation","title":"Boosting Medical Image Segmentation Performance with Adaptive Convolution Layer","date":"2024-04-17","arxiv_id":"2404.11361","n_code_links":1,"syntology":null},{"paper":"/paper/acc-unet-a-completely-convolutional-unet","title":"ACC-UNet: A Completely Convolutional UNet model for the 2020s","date":"2023-08-25","arxiv_id":"2308.13680","n_code_links":1,"syntology":null},{"paper":"/paper/lvit-language-meets-vision-transformer-in","title":"LViT: Language meets Vision Transformer in Medical Image Segmentation","date":"2022-06-29","arxiv_id":"2206.14718","n_code_links":1,"syntology":{"ran":1,"of":1,"unverified":0,"pointer_only":0}},{"paper":"/paper/uctransnet-rethinking-the-skip-connections-in","title":"UCTransNet: Rethinking the Skip Connections in U-Net from a Channel-wise Perspective with Transformer","date":"2021-09-09","arxiv_id":"2109.04335","n_code_links":3,"syntology":{"ran":1,"of":1,"unverified":0,"pointer_only":1}}],"papers_shown":4,"tasks":[{"task":"/task/image-segmentation","name":"Image Segmentation","papers":4},{"task":"/task/medical-image-segmentation","name":"Medical Image Segmentation","papers":4},{"task":"/task/semantic-segmentation","name":"Semantic Segmentation","papers":4},{"task":"/task/segmentation","name":"Segmentation","papers":2},{"task":"/task/decoder","name":"Decoder","papers":1},{"task":"/task/pseudo-label","name":"Pseudo Label","papers":1},{"task":"/task/unet-segmentation","name":"UNET Segmentation","papers":1},{"task":"/task/text-annotation","name":"text annotation","papers":1}],"tasks_shown":8,"n_tasks":8,"usage_by_year":[{"year":"2021","papers":1},{"year":"2022","papers":1},{"year":"2023","papers":1},{"year":"2024","papers":1}],"row_source":"embedded","archive":{"source":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","archive_url":"https://paperswithcode.com/method/uctransnet"},"syntology_read_at":"2026-09-24T18:15:14+00:00"}