{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/ted-net-convolution-free-t2t-vision","title":"TED-net: Convolution-free T2T Vision Transformer-based Encoder-decoder Dilation network for Low-dose CT Denoising","arxiv_id":"2106.04650","date":"2021-06-08","proceeding":null,"authors":["Dayang Wang","Zhan Wu","Hengyong Yu"],"abstract":"Low dose computed tomography is a mainstream for clinical applications. How-ever, compared to normal dose CT, in the low dose CT (LDCT) images, there are stronger noise and more artifacts which are obstacles for practical applications. In the last few years, convolution-based end-to-end deep learning methods have been widely used for LDCT image denoising. Recently, transformer has shown superior performance over convolution with more feature interactions. Yet its ap-plications in LDCT denoising have not been fully cultivated. Here, we propose a convolution-free T2T vision transformer-based Encoder-decoder Dilation net-work (TED-net) to enrich the family of LDCT denoising algorithms. The model is free of convolution blocks and consists of a symmetric encoder-decoder block with sole transformer. Our model is evaluated on the AAPM-Mayo clinic LDCT Grand Challenge dataset, and results show outperformance over the state-of-the-art denoising methods.","url_abs":"https://arxiv.org/abs/2106.04650v1","url_pdf":"https://arxiv.org/pdf/2106.04650v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"ted-net-convolution-free-t2t-vision","repo_url":"https://github.com/wdayang/TED-net","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"ted-net-convolution-free-t2t-vision","repo_url":"https://github.com/wdayang/ctformer","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"decoder","task_name":"Decoder"},{"task_slug":"denoising","task_name":"Denoising"},{"task_slug":"image-denoising","task_name":"Image Denoising"}],"methods":[{"method_slug":"convolution","method_name":"Convolution"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/2106.04650","atlas_url":"https://app.syntology.ai/?focus=2106.04650","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2106.04650"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/wdayang/ctformer","reach":{"status":"ok","spdx":"MIT"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/wdayang/TED-net","reach":{"status":"ok","spdx":"MIT"}}],"summary":{"ran":5,"ran_honours":4,"unverified":2},"by_repo_kind":{"listed":{"samples":11,"ran":9,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":0,"samples":[{"code_sha256_prefix":"a80807452d9fe8fe","entry":"compute_MSE","repo":"wdayang/TED-net","repo_kind":"listed","path":"measure.py","file_url":"https://github.com/wdayang/TED-net/blob/HEAD/measure.py","link_basis":"harvester_set","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"a80807452d9fe8fe"}},{"code_sha256_prefix":"5b7c1bbe0acccd8a","entry":"compute_RMSE","repo":"wdayang/TED-net","repo_kind":"listed","path":"measure.py","file_url":"https://github.com/wdayang/TED-net/blob/HEAD/measure.py","link_basis":"harvester_set","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"5b7c1bbe0acccd8a"}},{"code_sha256_prefix":"f6b944f50d3f15ae","entry":"count_parameters","repo":"wdayang/TED-net","repo_kind":"listed","path":"solver.py","file_url":"https://github.com/wdayang/TED-net/blob/HEAD/solver.py","link_basis":"harvester_set","language":"python","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"well_formed","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"f6b944f50d3f15ae"}},{"code_sha256_prefix":"5e5853c8260e5738","entry":"data_augmentation","repo":"wdayang/TED-net","repo_kind":"listed","path":"data_aug.py","file_url":"https://github.com/wdayang/TED-net/blob/HEAD/data_aug.py","link_basis":"harvester_set","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"5e5853c8260e5738"}},{"code_sha256_prefix":"e125665bbc856d11","entry":"get_pixels_hu","repo":"wdayang/TED-net","repo_kind":"listed","path":"prep.py","file_url":"https://github.com/wdayang/TED-net/blob/HEAD/prep.py","link_basis":"harvester_set","language":"python","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"well_formed","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"e125665bbc856d11"}},{"code_sha256_prefix":"ced6bc2bfdfe0059","entry":"get_sinusoid_encoding","repo":"wdayang/TED-net","repo_kind":"listed","path":"T2T_transformer_block.py","file_url":"https://github.com/wdayang/TED-net/blob/HEAD/T2T_transformer_block.py","link_basis":"harvester_set","language":"python","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"well_formed","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"ced6bc2bfdfe0059"}},{"code_sha256_prefix":"ffc0875b6223ff74","entry":"intra_att_f","repo":"wdayang/TED-net","repo_kind":"listed","path":"T2T_transformer_block.py","file_url":"https://github.com/wdayang/TED-net/blob/HEAD/T2T_transformer_block.py","link_basis":"harvester_set","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"ffc0875b6223ff74"}},{"code_sha256_prefix":"95b95b534ba46413","entry":"normalize_","repo":"wdayang/TED-net","repo_kind":"listed","path":"prep.py","file_url":"https://github.com/wdayang/TED-net/blob/HEAD/prep.py","link_basis":"harvester_set","language":"python","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"95b95b534ba46413"}},{"code_sha256_prefix":"37982feb54d8b289","entry":"split_arr","repo":"wdayang/TED-net","repo_kind":"listed","path":"solver.py","file_url":"https://github.com/wdayang/TED-net/blob/HEAD/solver.py","link_basis":"harvester_set","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"37982feb54d8b289"}},{"code_sha256_prefix":"4cb179d370f65009","entry":"agg_arr","repo":"wdayang/TED-net","repo_kind":"listed","path":"solver.py","file_url":"https://github.com/wdayang/TED-net/blob/HEAD/solver.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"4cb179d370f65009"}},{"code_sha256_prefix":"815caa6a87009529","entry":"compute_measure","repo":"wdayang/TED-net","repo_kind":"listed","path":"measure.py","file_url":"https://github.com/wdayang/TED-net/blob/HEAD/measure.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"815caa6a87009529"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}