4,144 research outputs found
A Survey on Deep Learning in Medical Image Analysis
Deep learning algorithms, in particular convolutional networks, have rapidly
become a methodology of choice for analyzing medical images. This paper reviews
the major deep learning concepts pertinent to medical image analysis and
summarizes over 300 contributions to the field, most of which appeared in the
last year. We survey the use of deep learning for image classification, object
detection, segmentation, registration, and other tasks and provide concise
overviews of studies per application area. Open challenges and directions for
future research are discussed.Comment: Revised survey includes expanded discussion section and reworked
introductory section on common deep architectures. Added missed papers from
before Feb 1st 201
A Deep Dive into Understanding Tumor Foci Classification using Multiparametric MRI Based on Convolutional Neural Network
Deep learning models have had a great success in disease classifications
using large data pools of skin cancer images or lung X-rays. However, data
scarcity has been the roadblock of applying deep learning models directly on
prostate multiparametric MRI (mpMRI). Although model interpretation has been
heavily studied for natural images for the past few years, there has been a
lack of interpretation of deep learning models trained on medical images. This
work designs a customized workflow for the small and imbalanced data set of
prostate mpMRI where features were extracted from a deep learning model and
then analyzed by a traditional machine learning classifier. In addition, this
work contributes to revealing how deep learning models interpret mpMRI for
prostate cancer patients stratification
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