Mass detection in digital breast tomosynthesis: Deep convolutional neural network with transfer learning from mammography

Purpose: Develop a computer-aided detection (CAD) system for masses in digital breast tomosynthesis (DBT) volume using a deep convolutional neural network (DCNN) with transfer learning from mammograms. Methods: A data set containing 2282 digitized film and digital mammograms and 324 DBT volumes were...

Full description

Saved in:

Bibliographic Details
Published in:	Medical physics (Lancaster) Vol. 43; no. 12; pp. 6654 - 6666
Main Authors:	Samala, Ravi K., Chan, Heang-Ping, Hadjiiski, Lubomir, Helvie, Mark A., Wei, Jun, Cha, Kenny
Format:	Journal Article
Language:	English
Published:	United States American Association of Physicists in Medicine 01.12.2016
Subjects:	Analysis of texture Artificial neural networks Biological material, e.g. blood, urine; Haemocytometers Breast Neoplasms - diagnostic imaging cancer computer‐aided detection convolutional neural network deep‐learning Diagnosis, Computer-Assisted digital breast tomosynthesis Digital computing or data processing equipment or methods, specially adapted for specific applications Digital tomosynthesis mammography feature extraction Film mammography image classification Image data processing or generation, in general Image detection systems Image Processing, Computer-Assisted - methods image segmentation image texture In which a programme is changed according to experience gained by the computer itself during a complete run; Learning machines Inference methods or devices Learning learning (artificial intelligence) Machine Learning Mammography mass medical image processing neural nets Neural Networks (Computer) QUANTITATIVE IMAGING AND IMAGE PROCESSING radiology Segmentation Testing procedures transfer learning tumours digital breast tomosynthesis transfer learning deep-learning computer-aided detection convolutional neural network mass
ISSN:	0094-2405, 2473-4209
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Be the first to leave a comment!