{"id":"https://openalex.org/W7161111552","doi":"https://doi.org/10.1145/3746467.3801524","title":"SA-SegFormer: Sequential Attention Mix-Transformer for NMSC Histopathology Segmentation and Uncertainty Estimation","display_name":"SA-SegFormer: Sequential Attention Mix-Transformer for NMSC Histopathology Segmentation and Uncertainty Estimation","publication_year":2026,"publication_date":"2026-04-23","ids":{"openalex":"https://openalex.org/W7161111552","doi":"https://doi.org/10.1145/3746467.3801524"},"language":null,"primary_location":{"id":"doi:10.1145/3746467.3801524","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3746467.3801524","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2026 ACM Southeast Conference","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1145/3746467.3801524","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5106163966","display_name":"Nishat Tasnim","orcid":null},"institutions":[{"id":"https://openalex.org/I172980758","display_name":"Kennesaw State University","ror":"https://ror.org/00jeqjx33","country_code":"US","type":"education","lineage":["https://openalex.org/I172980758"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Nishat Tasnim","raw_affiliation_strings":["Computer Science, Kennesaw State University, Marietta, GA, USA"],"raw_orcid":"https://orcid.org/0009-0006-9014-3743","affiliations":[{"raw_affiliation_string":"Computer Science, Kennesaw State University, Marietta, GA, USA","institution_ids":["https://openalex.org/I172980758"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5113542097","display_name":"Yong Shi","orcid":"https://orcid.org/0000-0001-7974-1079"},"institutions":[{"id":"https://openalex.org/I172980758","display_name":"Kennesaw State University","ror":"https://ror.org/00jeqjx33","country_code":"US","type":"education","lineage":["https://openalex.org/I172980758"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yong Shi","raw_affiliation_strings":["Computer Science, Kennesaw State University, Marietta, GA, USA"],"raw_orcid":"https://orcid.org/0000-0002-3980-1425","affiliations":[{"raw_affiliation_string":"Computer Science, Kennesaw State University, Marietta, GA, USA","institution_ids":["https://openalex.org/I172980758"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5106163966"],"corresponding_institution_ids":["https://openalex.org/I172980758"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.94908109,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"73","last_page":"82"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10862","display_name":"AI in cancer detection","score":0.7961999773979187,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T10862","display_name":"AI in cancer detection","score":0.7961999773979187,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T10392","display_name":"Cutaneous Melanoma Detection and Management","score":0.1386999934911728,"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/T12859","display_name":"Cell Image Analysis Techniques","score":0.01360000018030405,"subfield":{"id":"https://openalex.org/subfields/1304","display_name":"Biophysics"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/jaccard-index","display_name":"Jaccard index","score":0.8140000104904175},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.6589999794960022},{"id":"https://openalex.org/keywords/benchmarking","display_name":"Benchmarking","score":0.5651000142097473},{"id":"https://openalex.org/keywords/digital-pathology","display_name":"Digital pathology","score":0.5379999876022339},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.459199994802475},{"id":"https://openalex.org/keywords/interpretability","display_name":"Interpretability","score":0.3668000102043152},{"id":"https://openalex.org/keywords/histogram","display_name":"Histogram","score":0.3580000102519989},{"id":"https://openalex.org/keywords/entropy","display_name":"Entropy (arrow of time)","score":0.35679998993873596}],"concepts":[{"id":"https://openalex.org/C203519979","wikidata":"https://www.wikidata.org/wiki/Q865360","display_name":"Jaccard index","level":3,"score":0.8140000104904175},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6589999794960022},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6341000199317932},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6184999942779541},{"id":"https://openalex.org/C86251818","wikidata":"https://www.wikidata.org/wiki/Q816754","display_name":"Benchmarking","level":2,"score":0.5651000142097473},{"id":"https://openalex.org/C2777522853","wikidata":"https://www.wikidata.org/wiki/Q5276128","display_name":"Digital pathology","level":2,"score":0.5379999876022339},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.459199994802475},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3831999897956848},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3781999945640564},{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.3668000102043152},{"id":"https://openalex.org/C53533937","wikidata":"https://www.wikidata.org/wiki/Q185020","display_name":"Histogram","level":3,"score":0.3580000102519989},{"id":"https://openalex.org/C106301342","wikidata":"https://www.wikidata.org/wiki/Q4117933","display_name":"Entropy (arrow of time)","level":2,"score":0.35679998993873596},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.3472000062465668},{"id":"https://openalex.org/C141898687","wikidata":"https://www.wikidata.org/wiki/Q1501997","display_name":"Hausdorff distance","level":2,"score":0.32659998536109924},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3102000057697296},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.3070000112056732},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.27630001306533813},{"id":"https://openalex.org/C81669768","wikidata":"https://www.wikidata.org/wiki/Q2359161","display_name":"Precision and recall","level":2,"score":0.27469998598098755},{"id":"https://openalex.org/C2777789703","wikidata":"https://www.wikidata.org/wiki/Q192102","display_name":"Skin cancer","level":3,"score":0.2718999981880188},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.26829999685287476},{"id":"https://openalex.org/C194789388","wikidata":"https://www.wikidata.org/wiki/Q17855283","display_name":"CAD","level":2,"score":0.26100000739097595},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.26019999384880066},{"id":"https://openalex.org/C2780513914","wikidata":"https://www.wikidata.org/wiki/Q18210350","display_name":"Bottleneck","level":2,"score":0.2542000114917755},{"id":"https://openalex.org/C2779304628","wikidata":"https://www.wikidata.org/wiki/Q3503480","display_name":"Face (sociological concept)","level":2,"score":0.25290000438690186}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3746467.3801524","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3746467.3801524","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2026 ACM Southeast Conference","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3746467.3801524","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3746467.3801524","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2026 ACM Southeast Conference","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G6016837126","display_name":null,"funder_award_id":"2413540","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":12,"referenced_works":["https://openalex.org/W2778319782","https://openalex.org/W3107766008","https://openalex.org/W3141161369","https://openalex.org/W3184710018","https://openalex.org/W4229009122","https://openalex.org/W4396224765","https://openalex.org/W4402942447","https://openalex.org/W4403213543","https://openalex.org/W4404514656","https://openalex.org/W4405481199","https://openalex.org/W4407743777","https://openalex.org/W4409986253"],"related_works":[],"abstract_inverted_index":{"The":[0],"rising":[1],"global":[2],"incidence":[3],"of":[4,26,35,141,148,156],"Non-Melanoma":[5],"Skin":[6],"Cancer":[7],"(NMSC)":[8],"demands":[9],"automated":[10],"diagnostic":[11],"tools":[12],"that":[13,113,126],"ensure":[14],"both":[15],"segmentation":[16],"accuracy":[17],"and":[18,74,133,150,165],"clinical":[19],"trustworthiness.":[20],"To":[21],"address":[22],"the":[23,36,122],"specific":[24],"challenges":[25],"histopathological":[27],"analysis,":[28],"we":[29,100],"propose":[30],"SA-SegFormer,":[31],"a":[32,48,59,137,163],"specialized":[33],"evolution":[34],"SegFormer":[37],"architecture.":[38],"Distinct":[39],"from":[40],"standard":[41,134],"computer":[42],"vision":[43],"approaches,":[44],"our":[45],"method":[46],"introduces":[47],"sequential":[49],"refinement":[50],"topology":[51],"designed":[52],"for":[53,117,168],"high-noise":[54],"tissue":[55],"environments.":[56],"We":[57],"enforce":[58],"spatial-first":[60],"strategy":[61],"where":[62],"Spatial":[63],"Attention":[64],"Gates":[65],"(AG)":[66],"initially":[67],"suppress":[68],"non-malignant":[69],"artifacts,":[70],"such":[71],"as":[72,162],"inflammation":[73],"stromal":[75],"tissue,":[76],"before":[77],"Squeeze-and-Excitation":[78],"(SE)":[79],"blocks":[80],"apply":[81],"channel-wise":[82],"recalibration.":[83],"This":[84],"ordering":[85],"directs":[86],"model":[87],"capacity":[88],"toward":[89],"refining":[90],"malignant":[91],"boundaries":[92],"rather":[93],"than":[94],"processing":[95],"irrelevant":[96],"background":[97],"noise.":[98],"Furthermore,":[99],"integrate":[101],"Monte":[102],"Carlo":[103],"Dropout":[104],"to":[105],"quantify":[106],"epistemic":[107],"uncertainty,":[108],"producing":[109],"pixel-level":[110],"entropy":[111],"maps":[112],"highlight":[114],"ambiguous":[115],"regions":[116],"pathologist":[118],"review.":[119],"Benchmarking":[120],"on":[121],"NMSC":[123],"dataset":[124],"confirms":[125],"this":[127],"architectural":[128],"sequencing":[129],"outperforms":[130],"traditional":[131],"CNNs":[132],"Transformers,":[135],"achieving":[136],"mean":[138],"Intersection-over-Union":[139],"(mIoU)":[140],"89.62%,":[142],"an":[143,151],"Aggregated":[144],"Jaccard":[145],"Index":[146],"(AJI)":[147],"66.09%,":[149],"Average":[152],"Hausdorff":[153],"Distance":[154],"(AHD)":[155],"28.94.":[157],"These":[158],"metrics":[159],"validate":[160],"SA-SegFormer":[161],"precise":[164],"reliable":[166],"instrument":[167],"digital":[169],"pathology":[170],"workflows.":[171]},"counts_by_year":[],"updated_date":"2026-05-15T06:12:33.780692","created_date":"2026-05-15T00:00:00"}
