Ct2rep: automated radiology report generation for 3d medical imaging

Basit Öğe Kaydı
dc.contributor.authorHamamcı, İbrahim Ethem
dc.contributor.authorEr, Sezgin
dc.contributor.authorMenze, Bjoern
dc.date.accessioned2025-11-06T09:18:36Z
dc.date.available2025-11-06T09:18:36Z
dc.date.issued2024
dc.departmentİstanbul Medipol Üniversitesi, Rektörlük, Sağlık Bilim ve Teknolojileri Araştırma Enstitüsü
dc.description.abstractMedical imaging plays a crucial role in diagnosis, with radiology reports serving as vital documentation. Automating report generation has emerged as a critical need to alleviate the workload of radiologists. While machine learning has facilitated report generation for 2D medical imaging, extending this to 3D has been unexplored due to computational complexity and data scarcity. We introduce the first method to generate radiology reports for 3D medical imaging, specifically targeting chest CT volumes. Given the absence of comparable methods, we establish a baseline using an advanced 3D vision encoder in medical imaging to demonstrate our method’s effectiveness, which leverages a novel auto-regressive causal transformer. Furthermore, recognizing the benefits of leveraging information from previous visits, we augment CT2Rep with a cross-attention-based multi-modal fusion module and hierarchical memory, enabling the incorporation of longitudinal multimodal data. Access our code at https://github.com/ibrahimethemhamamci/CT2Rep.
dc.identifier.citationHamamcı, İ. E., Er, S. ve Menze, B. (2024). Ct2rep: automated radiology report generation for 3d medical imaging. 27th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) içinde (476-486. ss.). Palmeraie Conf Ctr, Marrakesh, Morocco, October 06-10, 2024. http://dx.doi.org/10.1007/978-3-031-72390-2_45
dc.identifier.doi10.1007/978-3-031-72390-2_45
dc.identifier.endpage486
dc.identifier.isbn9783031723896
dc.identifier.isbn9783031723902
dc.identifier.issn0302-9743
dc.identifier.issn1611-3349
dc.identifier.scopus2-s2.0-85208174893
dc.identifier.scopusqualityQ2
dc.identifier.startpage476
dc.identifier.urihttp://dx.doi.org/10.1007/978-3-031-72390-2_45
dc.identifier.urihttps://hdl.handle.net/20.500.12511/13168
dc.identifier.volume15012
dc.identifier.wosWOS:001344002100045
dc.identifier.wosqualityQ4
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorEr, Sezgin
dc.institutionauthorid0000-0001-7266-9844
dc.language.isoen
dc.relation.ispartof27th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI)
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subject3D Medical Imaging
dc.subjectChest CT Volume
dc.subjectCT-RATE Dataset
dc.subjectLongitudinal
dc.subjectRadiology Report
dc.subjectReport Generation
dc.subjectTransformers
dc.titleCt2rep: automated radiology report generation for 3d medical imaging
dc.typeConference Object

Dosyalar

Lisans paketi
Listeleniyor 1 - 1 / 1
Küçük Resim Yok
İsim:
license.txt
Boyut:
1.17 KB
Biçim:
Item-specific license agreed upon to submission
Açıklama:
İndir

Koleksiyon

Bildiri Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu
WoS İndeksli Yayınlar Koleksiyonu