1,818 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
Detection-aided liver lesion segmentation using deep learning
A fully automatic technique for segmenting the liver and localizing its
unhealthy tissues is a convenient tool in order to diagnose hepatic diseases
and assess the response to the according treatments. In this work we propose a
method to segment the liver and its lesions from Computed Tomography (CT) scans
using Convolutional Neural Networks (CNNs), that have proven good results in a
variety of computer vision tasks, including medical imaging. The network that
segments the lesions consists of a cascaded architecture, which first focuses
on the region of the liver in order to segment the lesions on it. Moreover, we
train a detector to localize the lesions, and mask the results of the
segmentation network with the positive detections. The segmentation
architecture is based on DRIU, a Fully Convolutional Network (FCN) with side
outputs that work on feature maps of different resolutions, to finally benefit
from the multi-scale information learned by different stages of the network.
The main contribution of this work is the use of a detector to localize the
lesions, which we show to be beneficial to remove false positives triggered by
the segmentation network. Source code and models are available at
https://imatge-upc.github.io/liverseg-2017-nipsws/ .Comment: NIPS 2017 Workshop on Machine Learning for Health (ML4H
Modeling the Intra-class Variability for Liver Lesion Detection using a Multi-class Patch-based CNN
Automatic detection of liver lesions in CT images poses a great challenge for
researchers. In this work we present a deep learning approach that models
explicitly the variability within the non-lesion class, based on prior
knowledge of the data, to support an automated lesion detection system. A
multi-class convolutional neural network (CNN) is proposed to categorize input
image patches into sub-categories of boundary and interior patches, the
decisions of which are fused to reach a binary lesion vs non-lesion decision.
For validation of our system, we use CT images of 132 livers and 498 lesions.
Our approach shows highly improved detection results that outperform the
state-of-the-art fully convolutional network. Automated computerized tools, as
shown in this work, have the potential in the future to support the
radiologists towards improved detection.Comment: To be presented at PatchMI: 3rd International Workshop on Patch-based
Techniques in Medical Imaging, MICCAI 201
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