{"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/saunet-shape-attentive-u-net-for","title":"SAUNet: Shape Attentive U-Net for Interpretable Medical Image Segmentation","arxiv_id":"2001.07645","date":"2020-01-21","proceeding":null,"authors":["Jesse Sun","Fatemeh Darbehani","Mark Zaidi","Bo wang"],"abstract":"Medical image segmentation is a difficult but important task for many clinical operations such as cardiac bi-ventricular volume estimation. More recently, there has been a shift to utilizing deep learning and fully convolutional neural networks (CNNs) to perform image segmentation that has yielded state-of-the-art results in many public benchmark datasets. Despite the progress of deep learning in medical image segmentation, standard CNNs are still not fully adopted in clinical settings as they lack robustness and interpretability. Shapes are generally more meaningful features than solely textures of images, which are features regular CNNs learn, causing a lack of robustness. Likewise, previous works surrounding model interpretability have been focused on post hoc gradient-based saliency methods. However, gradient-based saliency methods typically require additional computations post hoc and have been shown to be unreliable for interpretability. Thus, we present a new architecture called Shape Attentive U-Net (SAUNet) which focuses on model interpretability and robustness. The proposed architecture attempts to address these limitations by the use of a secondary shape stream that captures rich shape-dependent information in parallel with the regular texture stream. Furthermore, we suggest multi-resolution saliency maps can be learned using our dual-attention decoder module which allows for multi-level interpretability and mitigates the need for additional computations post hoc. Our method also achieves state-of-the-art results on the two large public cardiac MRI image segmentation datasets of SUN09 and AC17.","url_abs":"https://arxiv.org/abs/2001.07645v3","url_pdf":"https://arxiv.org/pdf/2001.07645v3.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":"saunet-shape-attentive-u-net-for","repo_url":"https://github.com/sunjesse/shape-attentive-unet","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"BSD-3-Clause"}},{"paper_slug":"saunet-shape-attentive-u-net-for","repo_url":"https://github.com/bowang-lab/shape-attentive-unet","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"saunet-shape-attentive-u-net-for","repo_url":"https://github.com/rexxxx1234/SAUNet-demo","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"BSD-3-Clause"}}],"tasks":[{"task_slug":"image-segmentation","task_name":"Image Segmentation"},{"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":"concatenated-skip-connection","method_name":"Concatenated Skip Connection"},{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"interpretability","method_name":"Interpretability"},{"method_slug":"max-pooling","method_name":"Max Pooling"},{"method_slug":"relu","method_name":"ReLU"},{"method_slug":"u-net","method_name":"U-Net"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/2001.07645","atlas_url":"https://app.syntology.ai/?focus=2001.07645","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2001.07645"}},"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/bowang-lab/shape-attentive-unet","reach":null},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/rexxxx1234/SAUNet-demo","reach":{"status":"ok","spdx":"BSD-3-Clause"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/sunjesse/shape-attentive-unet","reach":{"status":"ok","spdx":"BSD-3-Clause"}}],"summary":{"ran":8,"ran_draft_wrong":3,"unverified":4},"by_repo_kind":{"official":{"samples":12,"ran":8,"repositories":1},"listed":{"samples":3,"ran":3,"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":"24ba52fa75e98634","entry":"adaptive_avgmax_pool2d","repo":"sunjesse/shape-attentive-unet","repo_kind":"official","path":"models/adaptive_avgmax_pool.py","file_url":"https://github.com/sunjesse/shape-attentive-unet/blob/HEAD/models/adaptive_avgmax_pool.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"mcp_get_code":{"code_sha256":"24ba52fa75e98634"}},{"code_sha256_prefix":"5fd9109962bb3e30","entry":"batchnorm","repo":"sunjesse/shape-attentive-unet","repo_kind":"official","path":"models/attention_blocks.py","file_url":"https://github.com/sunjesse/shape-attentive-unet/blob/HEAD/models/attention_blocks.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"mcp_get_code":{"code_sha256":"5fd9109962bb3e30"}},{"code_sha256_prefix":"264280edf128c77a","entry":"calc_pad_same","repo":"sunjesse/shape-attentive-unet","repo_kind":"official","path":"models/custom_functions.py","file_url":"https://github.com/sunjesse/shape-attentive-unet/blob/HEAD/models/custom_functions.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"mcp_get_code":{"code_sha256":"264280edf128c77a"}},{"code_sha256_prefix":"1f54b5b7c5eed259","entry":"conv1x1_bn_relu","repo":"bowang-lab/shape-attentive-unet","repo_kind":"listed","path":"models/models.py","file_url":"https://github.com/bowang-lab/shape-attentive-unet/blob/HEAD/models/models.py","link_basis":"first_harvest_node","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"mcp_get_code":{"code_sha256":"1f54b5b7c5eed259"}},{"code_sha256_prefix":"dbcb53696bc43ef9","entry":"conv3x3","repo":"bowang-lab/shape-attentive-unet","repo_kind":"listed","path":"models/models.py","file_url":"https://github.com/bowang-lab/shape-attentive-unet/blob/HEAD/models/models.py","link_basis":"first_harvest_node","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"mcp_get_code":{"code_sha256":"dbcb53696bc43ef9"}},{"code_sha256_prefix":"ca893f9498143980","entry":"conv3x3","repo":"sunjesse/shape-attentive-unet","repo_kind":"official","path":"models/attention_blocks.py","file_url":"https://github.com/sunjesse/shape-attentive-unet/blob/HEAD/models/attention_blocks.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"mcp_get_code":{"code_sha256":"ca893f9498143980"}},{"code_sha256_prefix":"bf378f5e74f9c237","entry":"conv3x3_bn_relu","repo":"bowang-lab/shape-attentive-unet","repo_kind":"listed","path":"models/models.py","file_url":"https://github.com/bowang-lab/shape-attentive-unet/blob/HEAD/models/models.py","link_basis":"first_harvest_node","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"mcp_get_code":{"code_sha256":"bf378f5e74f9c237"}},{"code_sha256_prefix":"1205772b3f3f23ae","entry":"convert_to_grayscale","repo":"sunjesse/shape-attentive-unet","repo_kind":"official","path":"misc_functions.py","file_url":"https://github.com/sunjesse/shape-attentive-unet/blob/HEAD/misc_functions.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"mcp_get_code":{"code_sha256":"1205772b3f3f23ae"}},{"code_sha256_prefix":"3033223e4e93074d","entry":"format_np_output","repo":"sunjesse/shape-attentive-unet","repo_kind":"official","path":"misc_functions.py","file_url":"https://github.com/sunjesse/shape-attentive-unet/blob/HEAD/misc_functions.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"mcp_get_code":{"code_sha256":"3033223e4e93074d"}},{"code_sha256_prefix":"a0843c57fbf62259","entry":"gradient_central_diff","repo":"sunjesse/shape-attentive-unet","repo_kind":"official","path":"models/custom_functions.py","file_url":"https://github.com/sunjesse/shape-attentive-unet/blob/HEAD/models/custom_functions.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"mcp_get_code":{"code_sha256":"a0843c57fbf62259"}},{"code_sha256_prefix":"3a8857dbf960f1e4","entry":"pooling_factor","repo":"sunjesse/shape-attentive-unet","repo_kind":"official","path":"models/adaptive_avgmax_pool.py","file_url":"https://github.com/sunjesse/shape-attentive-unet/blob/HEAD/models/adaptive_avgmax_pool.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"mcp_get_code":{"code_sha256":"3a8857dbf960f1e4"}},{"code_sha256_prefix":"b27e3b79ace9291a","entry":"apply_colormap_on_image","repo":"sunjesse/shape-attentive-unet","repo_kind":"official","path":"misc_functions.py","file_url":"https://github.com/sunjesse/shape-attentive-unet/blob/HEAD/misc_functions.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"mcp_get_code":{"code_sha256":"b27e3b79ace9291a"}},{"code_sha256_prefix":"d5715d28a2cf2e5f","entry":"conv1x1","repo":"sunjesse/shape-attentive-unet","repo_kind":"official","path":"models/attention_blocks.py","file_url":"https://github.com/sunjesse/shape-attentive-unet/blob/HEAD/models/attention_blocks.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"mcp_get_code":{"code_sha256":"d5715d28a2cf2e5f"}},{"code_sha256_prefix":"c7421ba4abae271c","entry":"conv2d_same","repo":"sunjesse/shape-attentive-unet","repo_kind":"official","path":"models/custom_functions.py","file_url":"https://github.com/sunjesse/shape-attentive-unet/blob/HEAD/models/custom_functions.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"mcp_get_code":{"code_sha256":"c7421ba4abae271c"}},{"code_sha256_prefix":"249ebf36856edf1d","entry":"dice_loss","repo":"sunjesse/shape-attentive-unet","repo_kind":"official","path":"loss.py","file_url":"https://github.com/sunjesse/shape-attentive-unet/blob/HEAD/loss.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"mcp_get_code":{"code_sha256":"249ebf36856edf1d"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}