{"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/em-net-efficient-channel-and-frequency","title":"EM-Net: Efficient Channel and Frequency Learning with Mamba for 3D Medical Image Segmentation","arxiv_id":"2409.17675","date":"2024-09-26","proceeding":null,"authors":["Ao Chang","Jiajun Zeng","Ruobing Huang","Dong Ni"],"abstract":"Convolutional neural networks have primarily led 3D medical image segmentation but may be limited by small receptive fields. Transformer models excel in capturing global relationships through self-attention but are challenged by high computational costs at high resolutions. Recently, Mamba, a state space model, has emerged as an effective approach for sequential modeling. Inspired by its success, we introduce a novel Mamba-based 3D medical image segmentation model called EM-Net. It not only efficiently captures attentive interaction between regions by integrating and selecting channels, but also effectively utilizes frequency domain to harmonize the learning of features across varying scales, while accelerating training speed. Comprehensive experiments on two challenging multi-organ datasets with other state-of-the-art (SOTA) algorithms show that our method exhibits better segmentation accuracy while requiring nearly half the parameter size of SOTA models and 2x faster training speed.","url_abs":"https://arxiv.org/abs/2409.17675v1","url_pdf":"https://arxiv.org/pdf/2409.17675v1.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":"em-net-efficient-channel-and-frequency","repo_url":"https://github.com/zang0902/EM-Net","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"image-segmentation","task_name":"Image Segmentation"},{"task_slug":"mamba","task_name":"Mamba"},{"task_slug":"medical-image-segmentation","task_name":"Medical Image Segmentation"},{"task_slug":"segmentation","task_name":"Segmentation"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"}],"methods":[{"method_slug":"absolute-position-encodings","method_name":"Absolute Position Encodings"},{"method_slug":"adam","method_name":"Adam"},{"method_slug":"attention","method_name":"Attention"},{"method_slug":"bpe","method_name":"BPE"},{"method_slug":"dense-connections","method_name":"Dense Connections"},{"method_slug":"dropout","method_name":"Dropout"},{"method_slug":"label-smoothing","method_name":"Label Smoothing"},{"method_slug":"layer-normalization","method_name":"Layer Normalization"},{"method_slug":"linear-layer","method_name":"Linear Layer"},{"method_slug":"mamba","method_name":"Mamba"},{"method_slug":"multi-head-attention","method_name":"Multi-Head Attention"},{"method_slug":"position-wise-feed-forward-layer","method_name":"Position-Wise Feed-Forward Layer"},{"method_slug":"residual-connection","method_name":"Residual Connection"},{"method_slug":"softmax","method_name":"Softmax"},{"method_slug":"transformer","method_name":"Transformer"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=2409.17675","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2409.17675"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+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/zang0902/EM-Net","reach":{"status":"ok"}}],"summary":{"ran":6},"by_repo_kind":{"official":{"samples":6,"ran":6,"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":6,"samples":[{"code_sha256_prefix":"e98550ea6ba9250b","entry":"d2c","repo":"zang0902/EM-Net","repo_kind":"official","path":"cal_metrics.py","file_url":"https://github.com/zang0902/EM-Net/blob/HEAD/cal_metrics.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"e98550ea6ba9250b"}},{"code_sha256_prefix":"ca648ad3e7bd85d1","entry":"dc","repo":"zang0902/EM-Net","repo_kind":"official","path":"cal_metrics.py","file_url":"https://github.com/zang0902/EM-Net/blob/HEAD/cal_metrics.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"ca648ad3e7bd85d1"}},{"code_sha256_prefix":"7b0ecad70df7f175","entry":"dice","repo":"zang0902/EM-Net","repo_kind":"official","path":"trainer.py","file_url":"https://github.com/zang0902/EM-Net/blob/HEAD/trainer.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"7b0ecad70df7f175"}},{"code_sha256_prefix":"2d41b8a75d2fb5cc","entry":"jc","repo":"zang0902/EM-Net","repo_kind":"official","path":"cal_metrics.py","file_url":"https://github.com/zang0902/EM-Net/blob/HEAD/cal_metrics.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"2d41b8a75d2fb5cc"}},{"code_sha256_prefix":"5a6c05e1f6861a50","entry":"maybe_to_torch","repo":"zang0902/EM-Net","repo_kind":"official","path":"networks/transunet3d_network_architecture/neural_network.py","file_url":"https://github.com/zang0902/EM-Net/blob/HEAD/networks/transunet3d_network_architecture/neural_network.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"5a6c05e1f6861a50"}},{"code_sha256_prefix":"eb3650ce67c37e60","entry":"to_cuda","repo":"zang0902/EM-Net","repo_kind":"official","path":"networks/transunet3d_network_architecture/neural_network.py","file_url":"https://github.com/zang0902/EM-Net/blob/HEAD/networks/transunet3d_network_architecture/neural_network.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"eb3650ce67c37e60"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}