Mammographic breast density classification using a deep neural network: assessment based on inter-observer variability

Kaiser N, Fieselmann A, Vesal S, Ravikumar N, Ritschl L, Kappler S, Maier A (2019)

Publication Type: Conference contribution

Publication year: 2019

Journal

Proceedings of SPIE Society of Photo-optical Instrumentation Engineers (SPIE)

Publisher: SPIE-INT SOC OPTICAL ENGINEERING

City/Town: BELLINGHAM

Conference Proceedings Title: MEDICAL IMAGING 2019: IMAGE PERCEPTION, OBSERVER PERFORMANCE, AND TECHNOLOGY ASSESSMENT

Event location: San Diego, CA US

DOI: 10.1117/12.2513420

Authors with CRIS profile

Sulaiman Vesal Lehrstuhl für Informatik 5 (Mustererkennung) Nishant Ravikumar Lehrstuhl für Informatik 14 (Bild- und Sprachverarbeitung) Andreas Maier Lehrstuhl für Informatik 5 (Mustererkennung)

Related research project(s)

Development of multi-modal, multi-scale imaging framework for the early diagnosis of breast cancer March 1, 2017 - June 30, 2020

Involved external institutions

Siemens AG, Healthcare Sector DE Germany (DE)

How to cite

APA:

Kaiser, N., Fieselmann, A., Vesal, S., Ravikumar, N., Ritschl, L., Kappler, S., & Maier, A. (2019). Mammographic breast density classification using a deep neural network: assessment based on inter-observer variability. In MEDICAL IMAGING 2019: IMAGE PERCEPTION, OBSERVER PERFORMANCE, AND TECHNOLOGY ASSESSMENT. San Diego, CA, US: BELLINGHAM: SPIE-INT SOC OPTICAL ENGINEERING.

MLA:

Kaiser, N., et al. "Mammographic breast density classification using a deep neural network: assessment based on inter-observer variability." Proceedings of the Conference on Medical Imaging: Image Perception, Observer Performance, and Technology Assessment, San Diego, CA BELLINGHAM: SPIE-INT SOC OPTICAL ENGINEERING, 2019.

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