{"id":"https://openalex.org/W7155529927","doi":"https://doi.org/10.48550/arxiv.2604.21082","title":"Weighting What Matters: Boosting Sample Efficiency in Medical Report Generation via Token Reweighting","display_name":"Weighting What Matters: Boosting Sample Efficiency in Medical Report Generation via Token Reweighting","publication_year":2026,"publication_date":"2026-04-22","ids":{"openalex":"https://openalex.org/W7155529927","doi":"https://doi.org/10.48550/arxiv.2604.21082"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.21082","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.21082","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":"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.2604.21082","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5114352468","display_name":"Alexander Weers","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Weers, Alexander","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134539546","display_name":"Daniel Rueckert","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Rueckert, Daniel","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5005001205","display_name":"Martin J. Menten","orcid":"https://orcid.org/0000-0001-8261-7810"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Menten, Martin J.","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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.8938999772071838,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.8938999772071838,"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/T10028","display_name":"Topic Modeling","score":0.0210999995470047,"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/T13702","display_name":"Machine Learning in Healthcare","score":0.01889999955892563,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/security-token","display_name":"Security token","score":0.7287999987602234},{"id":"https://openalex.org/keywords/weighting","display_name":"Weighting","score":0.6876000165939331},{"id":"https://openalex.org/keywords/boosting","display_name":"Boosting (machine learning)","score":0.5685999989509583},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.462799996137619},{"id":"https://openalex.org/keywords/sample","display_name":"Sample (material)","score":0.43799999356269836},{"id":"https://openalex.org/keywords/salient","display_name":"Salient","score":0.4036000072956085},{"id":"https://openalex.org/keywords/quality","display_name":"Quality (philosophy)","score":0.3725000023841858}],"concepts":[{"id":"https://openalex.org/C48145219","wikidata":"https://www.wikidata.org/wiki/Q1335365","display_name":"Security token","level":2,"score":0.7287999987602234},{"id":"https://openalex.org/C183115368","wikidata":"https://www.wikidata.org/wiki/Q856577","display_name":"Weighting","level":2,"score":0.6876000165939331},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6777999997138977},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.5685999989509583},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5275999903678894},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.462799996137619},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.43799999356269836},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.43630000948905945},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4336000084877014},{"id":"https://openalex.org/C2780719617","wikidata":"https://www.wikidata.org/wiki/Q1030752","display_name":"Salient","level":2,"score":0.4036000072956085},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.3725000023841858},{"id":"https://openalex.org/C24756922","wikidata":"https://www.wikidata.org/wiki/Q1757694","display_name":"Data quality","level":3,"score":0.3334999978542328},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.33230000734329224},{"id":"https://openalex.org/C9357733","wikidata":"https://www.wikidata.org/wiki/Q6878417","display_name":"Missing data","level":2,"score":0.31290000677108765},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.3061000108718872},{"id":"https://openalex.org/C70136482","wikidata":"https://www.wikidata.org/wiki/Q13583781","display_name":"A-weighting","level":3,"score":0.28929999470710754},{"id":"https://openalex.org/C2780586882","wikidata":"https://www.wikidata.org/wiki/Q7520643","display_name":"Simple (philosophy)","level":2,"score":0.2856000065803528},{"id":"https://openalex.org/C58041806","wikidata":"https://www.wikidata.org/wiki/Q1660484","display_name":"Imputation (statistics)","level":3,"score":0.27959999442100525}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.21082","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.21082","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":"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.2604.21082","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.21082","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":"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":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Training":[0],"vision-language":[1],"models":[2],"(VLMs)":[3],"for":[4],"medical":[5],"report":[6,62,78],"generation":[7],"is":[8],"often":[9],"hindered":[10],"by":[11],"the":[12,21,44,48],"scarcity":[13],"of":[14,23],"high-quality":[15],"annotated":[16],"data.":[17,87],"This":[18],"work":[19],"evaluates":[20],"use":[22],"a":[24],"weighted":[25],"loss":[26,46],"function":[27],"to":[28,33,50,82],"improve":[29],"data":[30,74],"efficiency.":[31],"Compared":[32],"standard":[34],"cross-entropy":[35],"loss,":[36],"which":[37],"treats":[38],"all":[39],"token":[40],"prediction":[41],"errors":[42],"equally,":[43],"reweighted":[45],"shifts":[47],"focus":[49],"semantically":[51],"salient":[52],"tokens":[53],"with":[54,80],"outsized":[55],"clinical":[56],"importance.":[57],"In":[58],"experiments":[59],"on":[60],"ophthalmological":[61],"generation,":[63],"we":[64],"show":[65],"that":[66],"this":[67],"simple":[68],"method":[69],"improves":[70],"efficiency":[71],"across":[72],"multiple":[73],"scales,":[75],"achieving":[76],"similar":[77],"quality":[79],"up":[81],"ten":[83],"times":[84],"less":[85],"training":[86]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-04-25T00:00:00"}