892 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
Segmentation of Skin Lesions and their Attributes Using Multi-Scale Convolutional Neural Networks and Domain Specific Augmentations
Computer-aided diagnosis systems for classification of different type of skin
lesions have been an active field of research in recent decades. It has been
shown that introducing lesions and their attributes masks into lesion
classification pipeline can greatly improve the performance. In this paper, we
propose a framework by incorporating transfer learning for segmenting lesions
and their attributes based on the convolutional neural networks. The proposed
framework is based on the encoder-decoder architecture which utilizes a variety
of pre-trained networks in the encoding path and generates the prediction map
by combining multi-scale information in decoding path using a pyramid pooling
manner. To address the lack of training data and increase the proposed model
generalization, an extensive set of novel domain-specific augmentation routines
have been applied to simulate the real variations in dermoscopy images.
Finally, by performing broad experiments on three different data sets obtained
from International Skin Imaging Collaboration archive (ISIC2016, ISIC2017, and
ISIC2018 challenges data sets), we show that the proposed method outperforms
other state-of-the-art approaches for ISIC2016 and ISIC2017 segmentation task
and achieved the first rank on the leader-board of ISIC2018 attribute detection
task.Comment: 18 page
Radiological images and machine learning: trends, perspectives, and prospects
The application of machine learning to radiological images is an increasingly
active research area that is expected to grow in the next five to ten years.
Recent advances in machine learning have the potential to recognize and
classify complex patterns from different radiological imaging modalities such
as x-rays, computed tomography, magnetic resonance imaging and positron
emission tomography imaging. In many applications, machine learning based
systems have shown comparable performance to human decision-making. The
applications of machine learning are the key ingredients of future clinical
decision making and monitoring systems. This review covers the fundamental
concepts behind various machine learning techniques and their applications in
several radiological imaging areas, such as medical image segmentation, brain
function studies and neurological disease diagnosis, as well as computer-aided
systems, image registration, and content-based image retrieval systems.
Synchronistically, we will briefly discuss current challenges and future
directions regarding the application of machine learning in radiological
imaging. By giving insight on how take advantage of machine learning powered
applications, we expect that clinicians can prevent and diagnose diseases more
accurately and efficiently.Comment: 13 figure
A Deep Multi-task Learning Approach to Skin Lesion Classification
Skin lesion identification is a key step toward dermatological diagnosis.
When describing a skin lesion, it is very important to note its body site
distribution as many skin diseases commonly affect particular parts of the
body. To exploit the correlation between skin lesions and their body site
distributions, in this study, we investigate the possibility of improving skin
lesion classification using the additional context information provided by body
location. Specifically, we build a deep multi-task learning (MTL) framework to
jointly optimize skin lesion classification and body location classification
(the latter is used as an inductive bias). Our MTL framework uses the
state-of-the-art ImageNet pretrained model with specialized loss functions for
the two related tasks. Our experiments show that the proposed MTL based method
performs more robustly than its standalone (single-task) counterpart.Comment: AAAI 2017 Joint Workshop on Health Intelligence W3PHIAI 2017 (W3PHI &
HIAI), San Francisco, CA, 201
A Detection and Segmentation Architecture for Skin Lesion Segmentation on Dermoscopy Images
This report summarises our method and validation results for the ISIC
Challenge 2018 - Skin Lesion Analysis Towards Melanoma Detection - Task 1:
Lesion Segmentation. We present a two-stage method for lesion segmentation with
optimised training method and ensemble post-process. Our method achieves
state-of-the-art performance on lesion segmentation and we win the first place
in ISIC 2018 task1.Comment: 5 pages, 9 figures, Ranked 1st place in ISIC 2018 task1, title
updated and results adde
Propagating Uncertainty in Multi-Stage Bayesian Convolutional Neural Networks with Application to Pulmonary Nodule Detection
Motivated by the problem of computer-aided detection (CAD) of pulmonary
nodules, we introduce methods to propagate and fuse uncertainty information in
a multi-stage Bayesian convolutional neural network (CNN) architecture. The
question we seek to answer is "can we take advantage of the model uncertainty
provided by one deep learning model to improve the performance of the
subsequent deep learning models and ultimately of the overall performance in a
multi-stage Bayesian deep learning architecture?". Our experiments show that
propagating uncertainty through the pipeline enables us to improve the overall
performance in terms of both final prediction accuracy and model confidence.Comment: NIPS Workshop on Bayesian Deep Learning, 201
Deep Learning in Cardiology
The medical field is creating large amount of data that physicians are unable
to decipher and use efficiently. Moreover, rule-based expert systems are
inefficient in solving complicated medical tasks or for creating insights using
big data. Deep learning has emerged as a more accurate and effective technology
in a wide range of medical problems such as diagnosis, prediction and
intervention. Deep learning is a representation learning method that consists
of layers that transform the data non-linearly, thus, revealing hierarchical
relationships and structures. In this review we survey deep learning
application papers that use structured data, signal and imaging modalities from
cardiology. We discuss the advantages and limitations of applying deep learning
in cardiology that also apply in medicine in general, while proposing certain
directions as the most viable for clinical use.Comment: 27 pages, 2 figures, 10 table
GAN-based Synthetic Medical Image Augmentation for increased CNN Performance in Liver Lesion Classification
Deep learning methods, and in particular convolutional neural networks
(CNNs), have led to an enormous breakthrough in a wide range of computer vision
tasks, primarily by using large-scale annotated datasets. However, obtaining
such datasets in the medical domain remains a challenge. In this paper, we
present methods for generating synthetic medical images using recently
presented deep learning Generative Adversarial Networks (GANs). Furthermore, we
show that generated medical images can be used for synthetic data augmentation,
and improve the performance of CNN for medical image classification. Our novel
method is demonstrated on a limited dataset of computed tomography (CT) images
of 182 liver lesions (53 cysts, 64 metastases and 65 hemangiomas). We first
exploit GAN architectures for synthesizing high quality liver lesion ROIs. Then
we present a novel scheme for liver lesion classification using CNN. Finally,
we train the CNN using classic data augmentation and our synthetic data
augmentation and compare performance. In addition, we explore the quality of
our synthesized examples using visualization and expert assessment. The
classification performance using only classic data augmentation yielded 78.6%
sensitivity and 88.4% specificity. By adding the synthetic data augmentation
the results increased to 85.7% sensitivity and 92.4% specificity. We believe
that this approach to synthetic data augmentation can generalize to other
medical classification applications and thus support radiologists' efforts to
improve diagnosis.Comment: Preprint submitted to Neurocomputin
Detecting Scatteredly-Distributed, Small, andCritically Important Objects in 3D OncologyImaging via Decision Stratification
Finding and identifying scatteredly-distributed, small, and critically
important objects in 3D oncology images is very challenging. We focus on the
detection and segmentation of oncology-significant (or suspicious cancer
metastasized) lymph nodes (OSLNs), which has not been studied before as a
computational task. Determining and delineating the spread of OSLNs is
essential in defining the corresponding resection/irradiating regions for the
downstream workflows of surgical resection and radiotherapy of various cancers.
For patients who are treated with radiotherapy, this task is performed by
experienced radiation oncologists that involves high-level reasoning on whether
LNs are metastasized, which is subject to high inter-observer variations. In
this work, we propose a divide-and-conquer decision stratification approach
that divides OSLNs into tumor-proximal and tumor-distal categories. This is
motivated by the observation that each category has its own different
underlying distributions in appearance, size and other characteristics. Two
separate detection-by-segmentation networks are trained per category and fused.
To further reduce false positives (FP), we present a novel global-local network
(GLNet) that combines high-level lesion characteristics with features learned
from localized 3D image patches. Our method is evaluated on a dataset of 141
esophageal cancer patients with PET and CT modalities (the largest to-date).
Our results significantly improve the recall from to at FPs
per patient as compared to previous state-of-the-art methods. The highest
achieved OSLN recall of is clinically relevant and valuable.Comment: 14 pages, 4 Figure
Conditional Generative Refinement Adversarial Networks for Unbalanced Medical Image Semantic Segmentation
We propose a new generative adversarial architecture to mitigate imbalance
data problem in medical image semantic segmentation where the majority of
pixels belongs to a healthy region and few belong to lesion or non-health
region. A model trained with imbalanced data tends to bias toward healthy data
which is not desired in clinical applications and predicted outputs by these
networks have high precision and low sensitivity. We propose a new conditional
generative refinement network with three components: a generative, a
discriminative, and a refinement network to mitigate unbalanced data problem
through ensemble learning. The generative network learns to a segment at the
pixel level by getting feedback from the discriminative network according to
the true positive and true negative maps. On the other hand, the refinement
network learns to predict the false positive and the false negative masks
produced by the generative network that has significant value, especially in
medical application. The final semantic segmentation masks are then composed by
the output of the three networks. The proposed architecture shows
state-of-the-art results on LiTS-2017 for liver lesion segmentation, and two
microscopic cell segmentation datasets MDA231, PhC-HeLa. We have achieved
competitive results on BraTS-2017 for brain tumour segmentation
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