Performance comparison of deep learning and segmentation-based radiomic methods in the task of distinguishing benign and malignant breast lesions on DCE-MRI

Progress in Biomedical Optics and Imaging - Proceedings of SPIE

Volumn 10134, Issue , 2017, Pages

  Antropova, Natasha ^a   Huynh, Benjamin ^a   Giger, Maryellen ^a  

^a UNIVERSITY OF CHICAGO   (United States)

Author keywords

Breast malignancy; Computeraided diagnosis; Convolutional neural networks; Quantitative image analysis; Radiomics; Transfer learning

Indexed keywords

COMPUTER AIDED DIAGNOSIS; COMPUTER AIDED INSTRUCTION; CONVOLUTION; DEEP LEARNING; DEEP NEURAL NETWORKS; DIAGNOSIS; IMAGE SEGMENTATION; MAGNETIC LEVITATION VEHICLES; MAGNETIC RESONANCE; MAGNETIC RESONANCE IMAGING; MAMMOGRAPHY; NEURAL NETWORKS; ROUTERS;

BREAST MALIGNANCY; CONVOLUTIONAL NEURAL NETWORK; QUANTITATIVE IMAGE ANALYSIS; RADIOMICS; TRANSFER LEARNING;

MEDICAL IMAGING;

EID: 85020316343     PISSN: 16057422     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1117/12.2255582     Document Type: Conference Paper

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