{"id":"https://openalex.org/W7161727525","doi":"https://doi.org/10.48550/arxiv.2605.16519","title":"DepthPolyp: Pseudo-Depth Guided Lightweight Segmentation for Real-Time Colonoscopy","display_name":"DepthPolyp: Pseudo-Depth Guided Lightweight Segmentation for Real-Time Colonoscopy","publication_year":2026,"publication_date":"2026-05-15","ids":{"openalex":"https://openalex.org/W7161727525","doi":"https://doi.org/10.48550/arxiv.2605.16519"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.16519","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.16519","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2605.16519","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5136496829","display_name":"Zhuoyu Wu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wu, Zhuoyu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136500860","display_name":"Wenhui Ou","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ou, Wenhui","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136458778","display_name":"Lexi Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Lexi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5088938333","display_name":"Pei-Sze Tan","orcid":"https://orcid.org/0009-0007-9114-9976"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tan, Pei-Sze","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102515711","display_name":"DONGJUN WU","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wu, Dongjun","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5031308176","display_name":"Junhe Zhao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhao, Junhe","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136491496","display_name":"Wenqi Fang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Fang, Wenqi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5073449839","display_name":"Rapha\u00ebl C. -W. Phan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Phan, Rapha\u00ebl C. -W.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"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/T10552","display_name":"Colorectal Cancer Screening and Detection","score":0.8845999836921692,"subfield":{"id":"https://openalex.org/subfields/2730","display_name":"Oncology"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},"topics":[{"id":"https://openalex.org/T10552","display_name":"Colorectal Cancer Screening and Detection","score":0.8845999836921692,"subfield":{"id":"https://openalex.org/subfields/2730","display_name":"Oncology"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.044599998742341995,"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/T11775","display_name":"COVID-19 diagnosis using AI","score":0.013100000098347664,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.6888999938964844},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.607699990272522},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5073999762535095},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4189999997615814},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.41130000352859497},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.39660000801086426},{"id":"https://openalex.org/keywords/projection","display_name":"Projection (relational algebra)","score":0.38989999890327454},{"id":"https://openalex.org/keywords/code","display_name":"Code (set theory)","score":0.3776000142097473}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8098000288009644},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7662000060081482},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6888999938964844},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.6111000180244446},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.607699990272522},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5073999762535095},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4189999997615814},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.41130000352859497},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.39660000801086426},{"id":"https://openalex.org/C57493831","wikidata":"https://www.wikidata.org/wiki/Q3134666","display_name":"Projection (relational algebra)","level":2,"score":0.38989999890327454},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.3776000142097473},{"id":"https://openalex.org/C186967261","wikidata":"https://www.wikidata.org/wiki/Q5082128","display_name":"Mobile device","level":2,"score":0.3707999885082245},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.3686999976634979},{"id":"https://openalex.org/C2777402240","wikidata":"https://www.wikidata.org/wiki/Q6783436","display_name":"Masking (illustration)","level":2,"score":0.36250001192092896},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.3418999910354614},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.31459999084472656},{"id":"https://openalex.org/C65885262","wikidata":"https://www.wikidata.org/wiki/Q7429708","display_name":"Scale-space segmentation","level":4,"score":0.3086000084877014},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.30399999022483826},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.2791000008583069},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.27309998869895935},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.26030001044273376},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.25769999623298645}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.16519","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.16519","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2605.16519","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.16519","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":false,"raw_source_name":null,"raw_type":"Preprint"},"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":{"Accurate":[0],"polyp":[1],"segmentation":[2,55,136],"in":[3,44,174],"colonoscopy":[4],"is":[5],"essential":[6],"for":[7,72,79,87,171],"early":[8],"colorectal":[9],"cancer":[10],"detection,":[11],"yet":[12],"real-world":[13],"clinical":[14,176],"environments":[15],"pose":[16],"persistent":[17],"challenges":[18],"such":[19],"as":[20],"motion":[21],"blur,":[22],"specular":[23],"reflections,":[24],"and":[25,37,53,62,83,106,111,119,153,179],"illumination":[26],"instability.":[27],"Most":[28],"existing":[29],"methods":[30],"are":[31,182],"optimized":[32],"on":[33,58,103,108,131,164],"clean":[34,110],"benchmark":[35],"images":[36],"suffer":[38],"noticeable":[39],"performance":[40,137],"degradation":[41],"when":[42,101],"deployed":[43],"authentic":[45],"surgical":[46,128],"scenarios.":[47],"We":[48],"propose":[49],"DepthPolyp,":[50],"a":[51],"lightweight":[52,117],"robust":[54],"framework":[56],"based":[57],"pseudo-depth-guided":[59],"multi-task":[60],"learning":[61],"efficient":[63],"feature":[64,74,90],"modulation.":[65],"The":[66],"architecture":[67],"combines":[68],"hierarchical":[69],"Ghost":[70],"factorization":[71],"compact":[73],"generation,":[75],"Interleaved":[76],"Shuffle":[77],"Fusion":[78],"low-cost":[80],"cross-scale":[81],"interaction,":[82],"Dynamic":[84],"Group":[85],"Gating":[86],"adaptive":[88],"group-wise":[89],"weighting.":[91],"Extensive":[92],"experiments":[93],"demonstrate":[94],"that":[95],"DepthPolyp":[96,133],"achieves":[97,134],"strong":[98],"cross-dataset":[99],"generalization":[100],"trained":[102],"degraded":[104],"data":[105],"evaluated":[107],"both":[109],"noisy":[112],"target":[113],"domains,":[114],"consistently":[115],"outperforming":[116],"baselines":[118],"remaining":[120],"competitive":[121],"with":[122],"substantially":[123],"larger":[124,143],"models.":[125],"In":[126],"real":[127],"video":[129],"evaluation":[130],"PolypGen,":[132],"better":[135],"than":[138],"models":[139],"up":[140],"to":[141],"$20\\times$":[142],"while":[144],"preserving":[145],"real-time":[146,172],"inference":[147],"speed.":[148],"With":[149],"only":[150],"3.57M":[151],"parameters":[152],"0.86":[154],"GMACs,":[155],"the":[156],"proposed":[157],"method":[158],"runs":[159],"at":[160],"over":[161],"180":[162],"FPS":[163],"mobile":[165],"devices,":[166],"making":[167],"it":[168],"well":[169],"suited":[170],"deployment":[173],"resource-constrained":[175],"environments.":[177],"Code":[178],"pretrained":[180],"weights":[181],"available":[183],"at:":[184],"https://github.com/ReaganWu/DepthPolyp/":[185]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-05-20T00:00:00"}
