{"id":"https://openalex.org/W7133497040","doi":"https://doi.org/10.48550/arxiv.2603.03187","title":"ProSMA-UNet: Decoder Conditioning for Proximal-Sparse Skip Feature Selection","display_name":"ProSMA-UNet: Decoder Conditioning for Proximal-Sparse Skip Feature Selection","publication_year":2026,"publication_date":"2026-03-03","ids":{"openalex":"https://openalex.org/W7133497040","doi":"https://doi.org/10.48550/arxiv.2603.03187"},"language":"en","primary_location":{"id":"pmh:doi:10.48550/arxiv.2603.03187","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"publisher-specific-oa","license_id":"https://openalex.org/licenses/publisher-specific-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":null,"any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5065750724","display_name":"Chun-Wun Cheng","orcid":"https://orcid.org/0009-0007-3281-1202"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Cheng, Chun-Wun","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5128100137","display_name":"Yanqi Cheng","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Cheng, Yanqi","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5065454976","display_name":"Peiyuan Jing","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jing, Peiyuan","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5128065511","display_name":"Guang Yang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yang, Guang","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Montoya-Zegarra, Javier A.","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Montoya-Zegarra, Javier A.","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Sch\u00f6nlieb, Carola-Bibiane","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sch\u00f6nlieb, Carola-Bibiane","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5128142440","display_name":"Angelica I. Aviles-Rivero","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Aviles-Rivero, Angelica I.","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":7,"corresponding_author_ids":["https://openalex.org/A5065750724"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.6208000183105469,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.6208000183105469,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.06459999829530716,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11019","display_name":"Image Enhancement Techniques","score":0.04320000112056732,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.6288999915122986},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.5552999973297119},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.48010000586509705},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.44020000100135803},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.3905999958515167},{"id":"https://openalex.org/keywords/sigmoid-function","display_name":"Sigmoid function","score":0.3702000081539154},{"id":"https://openalex.org/keywords/relevance","display_name":"Relevance (law)","score":0.3625999987125397},{"id":"https://openalex.org/keywords/channel","display_name":"Channel (broadcasting)","score":0.36250001192092896},{"id":"https://openalex.org/keywords/feature-selection","display_name":"Feature selection","score":0.36239999532699585}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7964000105857849},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6392999887466431},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6288999915122986},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.5552999973297119},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.48010000586509705},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.44020000100135803},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.3905999958515167},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3828999996185303},{"id":"https://openalex.org/C81388566","wikidata":"https://www.wikidata.org/wiki/Q526668","display_name":"Sigmoid function","level":3,"score":0.3702000081539154},{"id":"https://openalex.org/C158154518","wikidata":"https://www.wikidata.org/wiki/Q7310970","display_name":"Relevance (law)","level":2,"score":0.3625999987125397},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.36250001192092896},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.36239999532699585},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.35260000824928284},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.33719998598098755},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.3260999917984009},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.3107999861240387},{"id":"https://openalex.org/C23746413","wikidata":"https://www.wikidata.org/wiki/Q1141379","display_name":"Seam carving","level":3,"score":0.29760000109672546},{"id":"https://openalex.org/C194544171","wikidata":"https://www.wikidata.org/wiki/Q21105679","display_name":"Gating","level":2,"score":0.2930000126361847},{"id":"https://openalex.org/C134765980","wikidata":"https://www.wikidata.org/wiki/Q879126","display_name":"Bitwise operation","level":2,"score":0.28630000352859497},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.28610000014305115},{"id":"https://openalex.org/C57273362","wikidata":"https://www.wikidata.org/wiki/Q576722","display_name":"Decoding methods","level":2,"score":0.2854999899864197},{"id":"https://openalex.org/C147764199","wikidata":"https://www.wikidata.org/wiki/Q6865248","display_name":"Minification","level":2,"score":0.26010000705718994},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.25929999351501465},{"id":"https://openalex.org/C2779679900","wikidata":"https://www.wikidata.org/wiki/Q25304431","display_name":"Saliency map","level":3,"score":0.2567000091075897},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.25270000100135803},{"id":"https://openalex.org/C17020691","wikidata":"https://www.wikidata.org/wiki/Q139677","display_name":"Operator (biology)","level":5,"score":0.251800000667572}],"mesh":[],"locations_count":3,"locations":[{"id":"pmh:doi:10.48550/arxiv.2603.03187","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"publisher-specific-oa","license_id":"https://openalex.org/licenses/publisher-specific-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},{"id":"pmh:oai:digitalcollection.zhaw.ch:11475/36407","is_oa":true,"landing_page_url":"https://digitalcollection.zhaw.ch/handle/11475/36407","pdf_url":null,"source":{"id":"https://openalex.org/S4306401811","display_name":"Z\u00fcrcher Hochschule f\u00fcr Angewandte Wissenschaften digital collection (Zurich University of Applied Sciences)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I200744771","host_organization_name":"ZHAW Zurich University of Applied Sciences","host_organization_lineage":["https://openalex.org/I200744771"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Text"},{"id":"doi:10.48550/arxiv.2603.03187","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.03187","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"pmh:doi:10.48550/arxiv.2603.03187","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"publisher-specific-oa","license_id":"https://openalex.org/licenses/publisher-specific-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Medical":[0],"image":[1],"segmentation":[2,180],"commonly":[3],"relies":[4],"on":[5,163,177],"U-shaped":[6],"encoder-decoder":[7],"architectures":[8],"such":[9],"as":[10,96],"U-Net,":[11],"where":[12],"skip":[13,29,94],"connections":[14],"preserve":[15],"fine":[16],"spatial":[17],"detail":[18],"by":[19,157],"injecting":[20],"high-resolution":[21],"encoder":[22],"features":[23,78],"into":[24],"the":[25],"decoder.":[26],"However,":[27],"these":[28],"pathways":[30],"also":[31],"propagate":[32],"low-level":[33],"textures,":[34],"background":[35],"clutter,":[36],"and":[37,119,166],"acquisition":[38],"noise,":[39],"allowing":[40],"irrelevant":[41,83,149],"information":[42],"to":[43,65,114],"bypass":[44],"deeper":[45],"semantic":[46],"filtering":[47],"--":[48],"an":[49,127],"issue":[50],"that":[51,75,140],"is":[52],"particularly":[53,173],"detrimental":[54],"in":[55],"low-contrast":[56],"clinical":[57],"imaging.":[58],"Although":[59],"attention":[60],"gates":[61],"have":[62],"been":[63],"introduced":[64],"address":[66],"this":[67],"limitation,":[68],"they":[69],"typically":[70],"produce":[71],"dense":[72],"sigmoid":[73],"masks":[74],"softly":[76],"reweight":[77],"rather":[79],"than":[80],"explicitly":[81],"removing":[82],"activations.":[84],"We":[85],"propose":[86],"ProSMA-UNet":[87],"(Proximal-Sparse":[88],"Multi-Scale":[89],"Attention":[90],"U-Net),":[91],"which":[92],"reformulates":[93],"gating":[95,155],"a":[97,105,136],"decoder-conditioned":[98,153],"sparse":[99],"feature":[100],"selection":[101],"problem.":[102],"ProSMA":[103,151],"constructs":[104],"multi-scale":[106],"compatibility":[107],"field":[108],"using":[109],"lightweight":[110],"depthwise":[111],"dilated":[112],"convolutions":[113],"capture":[115],"relevance":[116],"across":[117],"local":[118],"contextual":[120],"scales,":[121],"then":[122],"enforces":[123],"explicit":[124],"sparsity":[125],"via":[126],"$\\ell_1$":[128],"proximal":[129],"operator":[130],"with":[131,172],"learnable":[132],"per-channel":[133],"thresholds,":[134],"yielding":[135],"closed-form":[137],"soft-thresholding":[138],"gate":[139],"can":[141],"remove":[142],"noisy":[143],"responses.":[144],"To":[145],"further":[146],"suppress":[147],"semantically":[148],"channels,":[150],"incorporates":[152],"channel":[154],"driven":[156],"global":[158],"decoder":[159],"context.":[160],"Extensive":[161],"experiments":[162],"challenging":[164],"2D":[165],"3D":[167,179],"benchmarks":[168],"demonstrate":[169],"state-of-the-art":[170],"performance,":[171],"large":[174],"gains":[175],"($\\approx20$\\%)":[176],"difficult":[178],"tasks.":[181],"Project":[182],"page:":[183],"https://math-ml-x.github.io/ProSMA-UNet/":[184]},"counts_by_year":[],"updated_date":"2026-04-19T08:26:33.389920","created_date":"2026-03-05T00:00:00"}
