{"id":"https://openalex.org/W7134825930","doi":"https://doi.org/10.48550/arxiv.2603.07170","title":"Class Visualizations and Activation Atlases for Enhancing Interpretability in Deep Learning-Based Computational Pathology","display_name":"Class Visualizations and Activation Atlases for Enhancing Interpretability in Deep Learning-Based Computational Pathology","publication_year":2026,"publication_date":"2026-03-07","ids":{"openalex":"https://openalex.org/W7134825930","doi":"https://doi.org/10.48550/arxiv.2603.07170"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.07170","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.07170","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":"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"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2603.07170","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5045228374","display_name":"Marco Gustav","orcid":"https://orcid.org/0009-0009-3598-5720"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gustav, Marco","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5128680622","display_name":"Fabian Wolf","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wolf, Fabian","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5079522299","display_name":"Christina Glasner","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Glasner, Christina","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5038032832","display_name":"Nic G. Reitsam","orcid":"https://orcid.org/0000-0002-0070-3158"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Reitsam, Nic G.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5128656216","display_name":"Stefan Schulz","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Schulz, Stefan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5128672800","display_name":"Kira Aschenbroich","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Aschenbroich, Kira","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5019299921","display_name":"Bruno M\u00e4rkl","orcid":"https://orcid.org/0000-0002-7704-850X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"M\u00e4rkl, Bruno","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5073167797","display_name":"Sebastian Foersch","orcid":"https://orcid.org/0000-0002-4740-6900"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Foersch, Sebastian","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5128643494","display_name":"Jakob Nikolas Kather","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kather, Jakob Nikolas","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/T10862","display_name":"AI in cancer detection","score":0.8592000007629395,"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":0.8592000007629395,"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/T12859","display_name":"Cell Image Analysis Techniques","score":0.07020000368356705,"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"}},{"id":"https://openalex.org/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.01549999974668026,"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/interpretability","display_name":"Interpretability","score":0.9265999794006348},{"id":"https://openalex.org/keywords/visualization","display_name":"Visualization","score":0.698199987411499},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.5630000233650208},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5144000053405762},{"id":"https://openalex.org/keywords/ambiguity","display_name":"Ambiguity","score":0.4812999963760376},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4643999934196472},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.46070000529289246},{"id":"https://openalex.org/keywords/subclass","display_name":"Subclass","score":0.3959999978542328}],"concepts":[{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.9265999794006348},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.698199987411499},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6363000273704529},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5817000269889832},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.5630000233650208},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5144000053405762},{"id":"https://openalex.org/C2780522230","wikidata":"https://www.wikidata.org/wiki/Q1140419","display_name":"Ambiguity","level":2,"score":0.4812999963760376},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4643999934196472},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.46070000529289246},{"id":"https://openalex.org/C2776194381","wikidata":"https://www.wikidata.org/wiki/Q3965271","display_name":"Subclass","level":3,"score":0.3959999978542328},{"id":"https://openalex.org/C172367668","wikidata":"https://www.wikidata.org/wiki/Q6504956","display_name":"Data visualization","level":3,"score":0.35530000925064087},{"id":"https://openalex.org/C66024118","wikidata":"https://www.wikidata.org/wiki/Q1122506","display_name":"Computational model","level":2,"score":0.3172000050544739},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.31029999256134033},{"id":"https://openalex.org/C64073096","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Interactive visualization","level":3,"score":0.3082999885082245},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3034999966621399},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.28859999775886536},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.2856000065803528},{"id":"https://openalex.org/C189430467","wikidata":"https://www.wikidata.org/wiki/Q7293293","display_name":"Ranking (information retrieval)","level":2,"score":0.2833000123500824},{"id":"https://openalex.org/C26760741","wikidata":"https://www.wikidata.org/wiki/Q160402","display_name":"Perception","level":2,"score":0.27790001034736633},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.275299996137619},{"id":"https://openalex.org/C121608353","wikidata":"https://www.wikidata.org/wiki/Q12078","display_name":"Cancer","level":2,"score":0.25369998812675476}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.07170","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.07170","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2603.07170","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.07170","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":"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":0.414381742477417,"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"The":[0],"rapid":[1],"adoption":[2],"of":[3,12,232],"transformer-based":[4,69,222],"models":[5,224],"in":[6,134,202,221],"computational":[7],"pathology":[8,223],"has":[9,23],"enabled":[10],"prediction":[11],"molecular":[13],"and":[14,32,45,62,65,74,87,97,153,172,207,225],"clinical":[15],"biomarkers":[16],"from":[17,120],"H&E":[18],"whole-slide":[19],"images,":[20,188],"yet":[21],"interpretability":[22],"not":[24,50],"kept":[25],"pace":[26],"with":[27,79,131],"model":[28,71],"complexity.":[29,196],"While":[30],"attribution-":[31],"generative-based":[33],"methods":[34],"are":[35],"common,":[36],"feature":[37,215],"visualization":[38,60,216],"approaches":[39],"such":[40],"as":[41],"class":[42],"visualizations":[43],"(CVs)":[44],"activation":[46],"atlases":[47],"(AAs)":[48],"have":[49],"been":[51],"systematically":[52],"evaluated":[53],"for":[54,67,103,111,158,165,229],"these":[55],"models.":[56],"We":[57],"developed":[58],"a":[59,68,227],"framework":[61,228],"assessed":[63],"CVs":[64,100],"AAs":[66,138],"foundation":[70],"across":[72,235],"tissue":[73,116,159],"multi-organ":[75],"cancer":[76,113,135,167],"classification":[77,160],"tasks":[78],"increasing":[80],"label":[81,236],"granularity.":[82],"Four":[83],"pathologists":[84],"annotated":[85],"real":[86,187],"generated":[88],"images":[89],"to":[90,126],"quantify":[91],"inter-observer":[92],"agreement,":[93],"complemented":[94],"by":[95],"attribution":[96],"similarity":[98],"metrics.":[99],"preserved":[101],"recognizability":[102],"morphologically":[104],"distinct":[105],"tissues":[106],"but":[107],"showed":[108,210],"reduced":[109],"separability":[110,181],"overlapping":[112],"subclasses.":[114],"In":[115],"classification,":[117],"agreement":[118,185],"decreased":[119],"Fleiss":[121],"k":[122,127],"=":[123,128,162,170,178],"0.75":[124],"(scans)":[125],"0.31":[129],"(CVs),":[130],"similar":[132],"trends":[133],"subclass":[136,175],"tasks.":[137],"revealed":[139],"layer-dependent":[140],"organization:":[141],"coarse":[142,166],"tissue-level":[143],"concepts":[144],"formed":[145],"coherent":[146],"regions,":[147],"whereas":[148,205],"finer":[149],"subclasses":[150],"exhibited":[151],"dispersion":[152],"overlap.":[154],"Agreement":[155],"was":[156],"moderate":[157],"(k":[161,169,177],"0.58),":[163],"high":[164],"groupings":[168],"0.82),":[171],"low":[173],"at":[174],"level":[176],"0.11).":[179],"Atlas":[180],"closely":[182],"tracked":[183],"expert":[184,200],"on":[186],"indicating":[189],"that":[190],"representational":[191],"ambiguity":[192],"reflects":[193],"intrinsic":[194],"pathological":[195],"Attribution-based":[197],"metrics":[198,209],"approximated":[199],"variability":[201],"low-complexity":[203],"settings,":[204],"perceptual":[206],"distributional":[208],"limited":[211],"alignment.":[212],"Overall,":[213],"concept-level":[214],"reveals":[217],"structured":[218],"morphological":[219],"manifolds":[220],"provides":[226],"expert-centered":[230],"interrogation":[231],"learned":[233],"representations":[234],"granularities.":[237]},"counts_by_year":[],"updated_date":"2026-07-01T08:55:40.977307","created_date":"2026-03-11T00:00:00"}