{"id":"https://openalex.org/W7172021317","doi":"https://doi.org/10.1145/3774521.3774614","title":"RMA-Net: Residual Multiscale Attention CNN for Hyperspectral Brain Tissue Classification","display_name":"RMA-Net: Residual Multiscale Attention CNN for Hyperspectral Brain Tissue Classification","publication_year":2025,"publication_date":"2025-12-17","ids":{"openalex":"https://openalex.org/W7172021317","doi":"https://doi.org/10.1145/3774521.3774614"},"language":null,"primary_location":{"id":"doi:10.1145/3774521.3774614","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3774521.3774614","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 Sixteen Indian Conference on Computer Vision, Graphics and Image Processing","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1145/3774521.3774614","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100578802","display_name":"Raj Bahadur Singh","orcid":"https://orcid.org/0009-0002-0576-1733"},"institutions":[{"id":"https://openalex.org/I33552525","display_name":"LNM Institute of Information Technology","ror":"https://ror.org/03jp7rg16","country_code":"IN","type":"education","lineage":["https://openalex.org/I33552525"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Raj Bahadur Singh","raw_affiliation_strings":["Department of Computer Science and Engineering, The LNM Institute of Information Technology, Jaipur, Rajasthan, India"],"raw_orcid":"https://orcid.org/0009-0002-0576-1733","affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, The LNM Institute of Information Technology, Jaipur, Rajasthan, India","institution_ids":["https://openalex.org/I33552525"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5054797653","display_name":"A. S. Datta","orcid":"https://orcid.org/0009-0009-5834-1856"},"institutions":[{"id":"https://openalex.org/I33552525","display_name":"LNM Institute of Information Technology","ror":"https://ror.org/03jp7rg16","country_code":"IN","type":"education","lineage":["https://openalex.org/I33552525"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Aloke Datta","raw_affiliation_strings":["Department of Computer Science and Engineering, The LNM Institute of Information Technology, Jaipur, Rajasthan, India"],"raw_orcid":"https://orcid.org/0009-0009-5834-1856","affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, The LNM Institute of Information Technology, Jaipur, Rajasthan, India","institution_ids":["https://openalex.org/I33552525"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I33552525"],"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":"1","last_page":"9"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":null,"topics":[],"keywords":[{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.7628999948501587},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6901999711990356},{"id":"https://openalex.org/keywords/residual","display_name":"Residual","score":0.6575000286102295},{"id":"https://openalex.org/keywords/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.625},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5575000047683716},{"id":"https://openalex.org/keywords/block","display_name":"Block (permutation group theory)","score":0.5030999779701233},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5026000142097473},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.43970000743865967},{"id":"https://openalex.org/keywords/modality","display_name":"Modality (human\u2013computer interaction)","score":0.435699999332428}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.8086000084877014},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.7628999948501587},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6940000057220459},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6901999711990356},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.6575000286102295},{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.625},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5575000047683716},{"id":"https://openalex.org/C2777210771","wikidata":"https://www.wikidata.org/wiki/Q4927124","display_name":"Block (permutation group theory)","level":2,"score":0.5030999779701233},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5026000142097473},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.43970000743865967},{"id":"https://openalex.org/C2780226545","wikidata":"https://www.wikidata.org/wiki/Q6888030","display_name":"Modality (human\u2013computer interaction)","level":2,"score":0.435699999332428},{"id":"https://openalex.org/C2780719617","wikidata":"https://www.wikidata.org/wiki/Q1030752","display_name":"Salient","level":2,"score":0.4147999882698059},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4090000092983246},{"id":"https://openalex.org/C70518039","wikidata":"https://www.wikidata.org/wiki/Q16000077","display_name":"Dimensionality reduction","level":2,"score":0.3937000036239624},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3531000018119812},{"id":"https://openalex.org/C111030470","wikidata":"https://www.wikidata.org/wiki/Q1430460","display_name":"Curse of dimensionality","level":2,"score":0.3427000045776367},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.3156000077724457},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.29589998722076416},{"id":"https://openalex.org/C2944601119","wikidata":"https://www.wikidata.org/wiki/Q43744058","display_name":"Residual neural network","level":3,"score":0.28850001096725464},{"id":"https://openalex.org/C2986936838","wikidata":"https://www.wikidata.org/wiki/Q492038","display_name":"Brain tissue","level":2,"score":0.2773999869823456},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.26930001378059387},{"id":"https://openalex.org/C31601959","wikidata":"https://www.wikidata.org/wiki/Q931309","display_name":"Medical imaging","level":2,"score":0.2578999996185303},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.2531000077724457},{"id":"https://openalex.org/C58693492","wikidata":"https://www.wikidata.org/wiki/Q551875","display_name":"Neuroimaging","level":2,"score":0.25200000405311584}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3774521.3774614","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3774521.3774614","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 Sixteen Indian Conference on Computer Vision, Graphics and Image Processing","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3774521.3774614","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3774521.3774614","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 Sixteen Indian Conference on Computer Vision, Graphics and Image Processing","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":34,"referenced_works":["https://openalex.org/W2302541206","https://openalex.org/W2777214868","https://openalex.org/W2788703804","https://openalex.org/W2793848630","https://openalex.org/W2884368394","https://openalex.org/W2884741638","https://openalex.org/W2907100627","https://openalex.org/W2915280689","https://openalex.org/W2921862546","https://openalex.org/W2942170965","https://openalex.org/W2989871747","https://openalex.org/W2995165754","https://openalex.org/W3031696400","https://openalex.org/W3046476394","https://openalex.org/W3112034856","https://openalex.org/W3127167602","https://openalex.org/W3128646645","https://openalex.org/W3138516171","https://openalex.org/W3202024712","https://openalex.org/W4206550078","https://openalex.org/W4281633835","https://openalex.org/W4281921196","https://openalex.org/W4296184432","https://openalex.org/W4317206949","https://openalex.org/W4376271004","https://openalex.org/W4386362739","https://openalex.org/W4388665212","https://openalex.org/W4397022972","https://openalex.org/W4399816992","https://openalex.org/W4401673408","https://openalex.org/W4404549855","https://openalex.org/W4405270834","https://openalex.org/W4406728689","https://openalex.org/W4408146726"],"related_works":[],"abstract_inverted_index":{"Accurate":[0],"intraoperative":[1],"delineation":[2],"of":[3,35,108,116,123],"brain-tumour":[4],"margins":[5],"is":[6,78],"critical":[7],"for":[8,24,40,148,156],"maximising":[9],"resection":[10],"and":[11,32,118,139,153],"patient":[12],"outcomes.":[13],"Hyperspectral":[14],"Imaging":[15],"(HSI)":[16],"has":[17],"emerged":[18],"as":[19],"a":[20,48,60,81,119,145],"promising":[21],"non-invasive":[22],"modality":[23],"real-time":[25],"tissue":[26,74,151],"characterization,":[27],"but":[28],"the":[29,87,92,106],"high":[30],"dimensionality":[31],"complex":[33],"nature":[34],"HSI":[36,101],"data":[37],"pose":[38],"challenges":[39],"classification.":[41],"To":[42],"address":[43],"these":[44],"limitations,":[45],"we":[46],"propose":[47],"novel":[49],"Residual":[50,61],"Multiscale":[51,62],"Attention":[52],"Convolutional":[53],"Neural":[54],"Network":[55],"(RMA-Net).":[56],"The":[57,131],"architecture":[58],"features":[59,68],"Feature":[63],"(RMF)":[64],"block":[65],"that":[66,85,134],"captures":[67],"from":[69],"varying":[70],"receptive":[71],"fields,":[72],"enhancing":[73,157],"pattern":[75],"recognition.":[76],"This":[77],"coupled":[79],"with":[80],"spatial":[82],"attention":[83],"mechanism":[84],"recalibrates":[86],"feature":[88],"maps":[89],"to":[90],"emphasise":[91],"salient":[93],"areas.":[94],"Experiments":[95],"conducted":[96],"on":[97],"an":[98,113],"in":[99],"vivo":[100],"Brain":[102],"image":[103],"dataset":[104],"demonstrated":[105],"superiority":[107],"our":[109,135],"approach.":[110],"RMA-Net":[111,143],"achieved":[112],"overall":[114],"accuracy":[115],"99.55%":[117],"mean":[120],"Kappa":[121],"score":[122],"99.60%,":[124],"outperforming":[125],"seven":[126],"state-of-the-art":[127],"deep":[128],"learning":[129],"models.":[130],"results":[132],"confirmed":[133],"model":[136],"produced":[137],"coherent":[138],"accurate":[140],"classification":[141],"map.":[142],"sets":[144],"new":[146],"benchmark":[147],"HSI-based":[149],"brain":[150],"analysis":[152],"shows":[154],"potential":[155],"surgical":[158],"precision.":[159]},"counts_by_year":[],"updated_date":"2026-08-01T09:00:35.917206","created_date":"2026-08-01T00:00:00"}
