The importance of multi-modal imaging and clinical information for humans and AI-based algorithms to classify breast masses (INSPiRED 003): an international, multicenter analysis

被引：0
作者：
André Pfob
Chris Sidey-Gibbons
Richard G. Barr
Volker Duda
Zaher Alwafai
Corinne Balleyguier
Dirk-André Clevert
Sarah Fastner
Christina Gomez
Manuela Goncalo
Ines Gruber
Markus Hahn
André Hennigs
Panagiotis Kapetas
Sheng-Chieh Lu
Juliane Nees
Ralf Ohlinger
Fabian Riedel
Matthieu Rutten
Benedikt Schaefgen
Maximilian Schuessler
Anne Stieber
Riku Togawa
Mitsuhiro Tozaki
Sebastian Wojcinski
Cai Xu
Geraldine Rauch
Joerg Heil
Michael Golatta
机构：
[1] Heidelberg University Hospital,University Breast Unit, Department of Obstetrics and Gynecology
[2] The University of Texas MD Anderson Cancer Center,MD Anderson Center for INSPiRED Cancer Care (Integrated Systems for Patient
[3] The University of Texas MD Anderson Cancer Center,Reported Data)
[4] Northeast Ohio Medical University,Department of Symptom Research
[5] University of Marburg,Department of Radiology
[6] University of Greifswald,Department of Gynecology and Obstetrics
[7] Institut Gustave Roussy,Department of Gynecology and Obstetrics
[8] University Hospital Munich-Grosshadern,Department of Radiology
[9] University of Coimbra,Department of Radiology
[10] University of Tuebingen,Department of Radiology
[11] Medical University of Vienna,Department of Gynecology and Obstetrics
[12] Jeroen Bosch Hospital,Department of Biomedical Imaging and Image
[13] Radboud University Medical Center,Guided Therapy
[14] Heidelberg University Hospital,Department of Radiology
[15] Sagara Hospital,National Center for Tumor Diseases
[16] Klinikum Bielefeld Mitte GmbH,Department of Radiology
[17] Humboldt-Universität Zu Berlin,Department of Gynecology and Obstetrics, Breast Cancer Center
来源：
European Radiology | 2022年 / 32卷
关键词：
Breast cancer; Ultrasonography; Machine learning; Artificial intelligence;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
引用
收藏
页码：4101 / 4115
页数：14
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