{"id":"https://openalex.org/W7225588941","doi":"https://doi.org/10.5281/zenodo.24046114","title":"NeuroSeg: A Bi-Hemispheric Spectral-Attention U-Net Workspace with Signed Distance Boundary Supervision, Bayesian Uncertainty Calibration, and Cross-Modal Neuro-Hematology Fusion","display_name":"NeuroSeg: A Bi-Hemispheric Spectral-Attention U-Net Workspace with Signed Distance Boundary Supervision, Bayesian Uncertainty Calibration, and Cross-Modal Neuro-Hematology Fusion","publication_year":2026,"publication_date":"2026-11-08","ids":{"openalex":"https://openalex.org/W7225588941","doi":"https://doi.org/10.5281/zenodo.24046114"},"language":"en","primary_location":{"id":"doi:10.5281/zenodo.24046114","doi":"https://doi.org/10.5281/zenodo.24046114","is_oa":true,"landing_page_url":"https://doi.org/10.5281/zenodo.24046114","pdf_url":null,"source":{"id":"https://openalex.org/S4306400562","display_name":"Zenodo (CERN European Organization for Nuclear Research)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I67311998","host_organization_name":"European Organization for Nuclear Research","host_organization_lineage":["https://openalex.org/I67311998"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"volume":null,"type":"preprint","is_traditional":true,"indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.5281/zenodo.24046114","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5160243182","display_name":"Bhavik Achotani","orcid":"https://orcid.org/0009-0006-3098-6175","observed_orcids":["https://orcid.org/0009-0006-3098-6175"]},"institutions":[{"id":"https://openalex.org/I169877490","display_name":"University of Mumbai","ror":"https://ror.org/032hdk172","country_code":"IN","type":"education","lineage":["https://openalex.org/I169877490"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"Achotani, Bhavik","raw_affiliation_strings":["Department of Information Technology, Thadomal Shahani Engineering College, University of Mumbai"],"raw_orcid":"https://orcid.org/0009-0006-3098-6175","affiliations":[{"raw_affiliation_string":"Department of Information Technology, Thadomal Shahani Engineering College, University of Mumbai","institution_ids":["https://openalex.org/I169877490"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5160243182"],"corresponding_institution_ids":["https://openalex.org/I169877490"],"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/T12702","display_name":"Brain Tumor Detection and Classification","score":0.8564000129699707,"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/T12702","display_name":"Brain Tumor Detection and Classification","score":0.8564000129699707,"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/T10052","display_name":"Medical Image Segmentation Techniques","score":0.12380000203847885,"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/T12422","display_name":"Radiomics and Machine Learning in Medical Imaging","score":0.003700000001117587,"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/glioma-segmentation","display_name":"glioma segmentation","score":0.7229999899864197},{"id":"https://openalex.org/keywords/neuro-oncology-imaging","display_name":"neuro-oncology imaging","score":0.8330000042915344},{"id":"https://openalex.org/keywords/spectral-attention","display_name":"spectral attention","score":0.4440000057220459},{"id":"https://openalex.org/keywords/monte-carlo-dropout","display_name":"Monte Carlo dropout","score":0.8259999752044678},{"id":"https://openalex.org/keywords/cross-modal-fusion","display_name":"cross-modal fusion","score":0.5329999923706055},{"id":"https://openalex.org/keywords/liquid-biopsy","display_name":"liquid biopsy","score":0.8700000047683716},{"id":"https://openalex.org/keywords/anaplastic-glioma","display_name":"anaplastic glioma","score":0.6069999933242798},{"id":"https://openalex.org/keywords/low-grade-glioma","display_name":"low-grade glioma","score":0.7609999775886536},{"id":"https://openalex.org/keywords/t2-flair","display_name":"T2-FLAIR","score":0.7039999961853027},{"id":"https://openalex.org/keywords/grad-cam","display_name":"Grad-CAM","score":0.5889999866485596},{"id":"https://openalex.org/keywords/multimodal-medical-imaging","display_name":"multimodal medical imaging","score":0.39800000190734863}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5752000212669373},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5385000109672546},{"id":"https://openalex.org/C117251300","wikidata":"https://www.wikidata.org/wiki/Q1849855","display_name":"Parametric statistics","level":2,"score":0.4000999927520752},{"id":"https://openalex.org/C62354387","wikidata":"https://www.wikidata.org/wiki/Q875399","display_name":"Boundary (topology)","level":2,"score":0.3986999988555908},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3817000091075897},{"id":"https://openalex.org/C106301342","wikidata":"https://www.wikidata.org/wiki/Q4117933","display_name":"Entropy (arrow of time)","level":2,"score":0.3799999952316284},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.37880000472068787},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.3702999949455261},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.33649998903274536},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.3287999927997589},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.32120001316070557},{"id":"https://openalex.org/C31601959","wikidata":"https://www.wikidata.org/wiki/Q931309","display_name":"Medical imaging","level":2,"score":0.3125999867916107},{"id":"https://openalex.org/C183115368","wikidata":"https://www.wikidata.org/wiki/Q856577","display_name":"Weighting","level":2,"score":0.30979999899864197},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.29750001430511475},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.27709999680519104},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.2736000120639801},{"id":"https://openalex.org/C19499675","wikidata":"https://www.wikidata.org/wiki/Q232207","display_name":"Monte Carlo method","level":2,"score":0.2734000086784363},{"id":"https://openalex.org/C2780513914","wikidata":"https://www.wikidata.org/wiki/Q18210350","display_name":"Bottleneck","level":2,"score":0.26809999346733093},{"id":"https://openalex.org/C82990744","wikidata":"https://www.wikidata.org/wiki/Q166194","display_name":"RGB color model","level":2,"score":0.2669000029563904},{"id":"https://openalex.org/C207201462","wikidata":"https://www.wikidata.org/wiki/Q182505","display_name":"Bayes' theorem","level":3,"score":0.2630999982357025},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.26179999113082886},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.25119999051094055},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.25040000677108765}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.5281/zenodo.24046114","doi":"https://doi.org/10.5281/zenodo.24046114","is_oa":true,"landing_page_url":"https://doi.org/10.5281/zenodo.24046114","pdf_url":null,"source":{"id":"https://openalex.org/S4306400562","display_name":"Zenodo (CERN European Organization for Nuclear Research)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I67311998","host_organization_name":"European Organization for Nuclear Research","host_organization_lineage":["https://openalex.org/I67311998"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},{"id":"doi:10.5281/zenodo.24046115","doi":"https://doi.org/10.5281/zenodo.24046115","is_oa":true,"landing_page_url":"https://doi.org/10.5281/zenodo.24046115","pdf_url":null,"source":{"id":"https://openalex.org/S4306400562","display_name":"Zenodo (CERN European Organization for Nuclear Research)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I67311998","host_organization_name":"European Organization for Nuclear Research","host_organization_lineage":["https://openalex.org/I67311998"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.5281/zenodo.24046114","doi":"https://doi.org/10.5281/zenodo.24046114","is_oa":true,"landing_page_url":"https://doi.org/10.5281/zenodo.24046114","pdf_url":null,"source":{"id":"https://openalex.org/S4306400562","display_name":"Zenodo (CERN European Organization for Nuclear Research)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I67311998","host_organization_name":"European Organization for Nuclear Research","host_organization_lineage":["https://openalex.org/I67311998"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"score":0.9391000270843506,"id":"https://metadata.un.org/sdg/3","display_name":"Good health and well-being"}],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W7203945595","https://openalex.org/W7224461539","https://openalex.org/W7126026112","https://openalex.org/W2955805370","https://openalex.org/W7129479394","https://openalex.org/W7171661571","https://openalex.org/W7164167756","https://openalex.org/W4404065878","https://openalex.org/W7225137002","https://openalex.org/W7204574877"],"abstract_inverted_index":{"In":[0,85,204],"this":[1],"work,":[2],"we":[3],"developed":[4],"NeuroSeg,":[5],"an":[6,53,116,215],"integrated":[7],"clinical":[8,42,47],"neuro-oncology":[9],"imaging,":[10],"uncertainty-quantification,":[11],"and":[12,19,37,41,49,58,61,65,139,178,198,224,248,267,316,353,370,381],"multimodal":[13,63,329],"blood-biomarker":[14],"fusion":[15,337],"workspace":[16],"for":[17,258,358],"lower-grade":[18],"anaplastic":[20],"glioma":[21],"assessment.":[22],"The":[23],"system":[24],"is":[25],"organized":[26],"as":[27],"a":[28,31,45,62,92,140,183,209,221,268,275],"four-tier":[29],"architecture:":[30],"React":[32],"19":[33],"interactive":[34],"Picture":[35],"Archiving":[36],"Communication":[38],"System":[39],"(PACS)":[40],"triage":[43],"client,":[44],"FastAPI":[46],"validation":[48],"report-synthesis":[50],"service":[51],"tier,":[52,60],"in-process":[54],"PyTorch":[55],"neural":[56],"inference":[57,155],"explainability":[59],"dataset":[64],"model-checkpoint":[66],"persistence":[67],"tier":[68],"spanning":[69],"N":[70],"=":[71,114,134,151,333,377],"12,000":[72],"parametric":[73],"records":[74],"across":[75,126,375],"300":[76],"patients":[77],"alongside":[78],"120":[79],"paired":[80,188],"axial":[81],"T2-FLAIR":[82],"TIFF":[83],"scans.":[84],"the":[86,127,157,205,327],"spatial":[87,160],"imaging":[88],"plane,":[89,208],"NeuroSeg":[90,280],"extends":[91],"four-stage":[93],"residual":[94],"encoder-decoder":[95],"backbone":[96],"(43,991,373":[97],"trainable":[98],"parameters)":[99],"with":[100,189,220],"2D":[101],"Real":[102],"Fast":[103],"Fourier":[104],"Transform":[105],"(rfft2)":[106],"radial":[107],"Gaussian":[108],"high-pass":[109],"Spectral":[110],"Attention":[111],"Gates":[112],"(sigma":[113],"0.08),":[115],"eight-head":[117],"Bi-Hemispheric":[118],"Contralateral":[119],"Cross-Attention":[120],"bottleneck":[121],"that":[122],"mirrors":[123],"feature":[124],"tokens":[125],"sagittal":[128],"midline,":[129],"four-scale":[130],"Deep":[131],"Supervision":[132],"(w":[133],"[0.5333,":[135],"0.2667,":[136],"0.1333,":[137],"0.0667]),":[138],"GPU-accelerated":[141],"iterative":[142],"morphological":[143],"Signed":[144],"Distance":[145,306],"Map":[146],"(SDM)":[147],"boundary":[148,185],"loss":[149],"(K":[150],"8":[152],"steps).":[153],"At":[154],"time,":[156],"engine":[158],"executes":[159],"Monte":[161],"Carlo":[162],"(MC)":[163],"dropout":[164],"(T":[165],"in":[166],"[2,":[167],"16]":[168],"stochastic":[169],"forward":[170],"passes)":[171],"to":[172,293,302,311,323],"decompose":[173],"voxel-wise":[174],"predictive":[175],"Shannon":[176],"entropy":[177],"epistemic":[179],"mutual":[180],"information":[181],"into":[182],"visual":[184],"uncertainty":[186],"halo,":[187],"higher-order":[190],"Grad-CAM++":[191],"saliency":[192],"maps":[193],"on":[194],"decoder":[195],"stage":[196],"dec1":[197],"3D":[199],"RECIST":[200],"1.1":[201],"volumetric":[202],"reconstruction.":[203],"systemic":[206],"hematology":[207,223],"Gated":[210],"Multimodal":[211],"Unit":[212],"(CrossModalGatedFusionNet)":[213],"fuses":[214],"8-dimensional":[216],"MRI":[217],"volumetric/boundary":[218],"vector":[219],"14-dimensional":[222],"astroglial":[225],"liquid-biopsy":[226],"vector\u2014comprising":[227],"Hemoglobin,":[228],"WBC,":[229],"ANC,":[230],"ALC,":[231],"Platelets,":[232],"Prothrombin":[233],"INR,":[234],"D-Dimer,":[235],"Serum":[236,241,243],"Sodium,":[237],"Fasting":[238],"Glucose,":[239],"hs-CRP,":[240],"LDH,":[242],"GFAP,":[244],"Neutrophil-to-Lymphocyte":[245],"Ratio":[246],"(NLR),":[247],"Systemic":[249],"Immune-Inflammation":[250],"Index":[251],"(SII)\u2014while":[252],"enforcing":[253],"deterministic":[254],"Common":[255],"Terminology":[256],"Criteria":[257],"Adverse":[259],"Events":[260],"(CTCAE":[261],"v5.0)":[262],"Temozolomide":[263],"(TMZ)":[264],"dosing":[265],"gates":[266],"five-point":[269],"Pre-Craniotomy":[270],"Neurosurgical":[271],"Clearance":[272],"shield.":[273],"Across":[274],"five-stage":[276],"architectural":[277],"ablation":[278],"study,":[279],"improved":[281],"segmentation":[282],"Dice":[283],"Similarity":[284],"Coefficient":[285],"(DSC)":[286],"from":[287,300,308,321],"0.8310":[288],"(vanilla":[289],"U-Net":[290],"baseline":[291],"B0)":[292],"0.9184":[294],"(+8.74%),":[295],"Intersection":[296],"over":[297],"Union":[298],"(IoU)":[299],"0.7425":[301],"0.8512,":[303],"95th-percentile":[304],"Hausdorff":[305],"(HD95)":[307],"8.64":[309],"mm":[310,313],"3.42":[312],"(-5.22":[314],"mm),":[315],"Expected":[317],"Calibration":[318],"Error":[319],"(ECE)":[320],"0.0680":[322],"0.0192":[324],"(-71.8%).":[325],"On":[326],"held-out":[328],"test":[330],"split":[331],"(N_test":[332],"1,500),":[334],"cross-modal":[335],"gated":[336],"achieved":[338],"94.33%":[339],"four-class":[340],"differential":[341],"diagnosis":[342],"accuracy":[343],"(disambiguating":[344],"Diffuse":[345],"Glioma,":[346],"Pyogenic":[347],"Cerebral":[348],"Abscess,":[349],"Primary":[350],"CNS":[351,363],"Lymphoma,":[352],"Tumefactive":[354],"Demyelination":[355],"vs.":[356],"76.40%":[357],"unimodal":[359],"B0),":[360],"99.87%":[361],"WHO":[362],"Grade":[364],"stratification":[365],"accuracy,":[366],"0.9640":[367],"macro":[368],"ROC-AUC,":[369],"zero":[371],"false-clearance":[372],"safety":[373],"leaks":[374],"N_adv":[376],"40":[378],"adversarial":[379],"hematologic":[380],"out-of-distribution":[382],"upload":[383],"injections.":[384]},"counts_by_year":[],"updated_date":"2026-11-10T11:39:43.021295","created_date":"2026-11-09T00:00:00"}