{"ID":2836168,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.20956","arxiv_id":"2511.20956","title":"BUSTR: Breast Ultrasound Text Reporting with a Descriptor-Aware Vision-Language Model","abstract":"Automated radiology report generation (RRG) for breast ultrasound (BUS) is limited by the lack of paired image-report datasets and the risk of hallucinations from large language models. We propose BUSTR, a multitask vision-language framework that generates BUS reports without requiring paired image-report supervision. BUSTR constructs reports from structured descriptors (e.g., BI-RADS, pathology, histology) and radiomics features, learns descriptor-aware visual representations with a multi-head Swin encoder trained using a multitask loss over dataset-specific descriptor sets, and aligns visual and textual tokens via a dual-level objective that combines token-level cross-entropy with a cosine-similarity alignment loss between input and output representations. We evaluate BUSTR on two public BUS datasets, BrEaST and BUS-BRA, which differ in size and available descriptors. Across both datasets, BUSTR consistently improves standard natural language generation metrics and clinical efficacy metrics, particularly for key targets such as BI-RADS category and pathology. Our results show that this descriptor-aware vision model, trained with a combined token-level and alignment loss, improves both automatic report metrics and clinical efficacy without requiring paired image-report data. The source code can be found at https://github.com/AAR-UNLV/BUSTR","short_abstract":"Automated radiology report generation (RRG) for breast ultrasound (BUS) is limited by the lack of paired image-report datasets and the risk of hallucinations from large language models. We propose BUSTR, a multitask vision-language framework that generates BUS reports without requiring paired image-report supervision....","url_abs":"https://arxiv.org/abs/2511.20956","url_pdf":"https://arxiv.org/pdf/2511.20956v1","authors":"[\"Rawa Mohammed\",\"Mina Attin\",\"Bryar Shareef\"]","published":"2025-11-26T01:22:29Z","proceeding":"cs.CV","tasks":"[\"cs.CV\",\"cs.AI\",\"cs.LG\"]","methods":"[\"Language Model\"]","has_code":false,"code_links":[{"ID":606574,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2836168,"paper_url":"https://arxiv.org/abs/2511.20956","paper_title":"BUSTR: Breast Ultrasound Text Reporting with a Descriptor-Aware Vision-Language Model","repo_url":"https://github.com/AAR-UNLV/BUSTR","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}