479 research outputs found
Improving the Segmentation of Anatomical Structures in Chest Radiographs using U-Net with an ImageNet Pre-trained Encoder
Accurate segmentation of anatomical structures in chest radiographs is
essential for many computer-aided diagnosis tasks. In this paper we investigate
the latest fully-convolutional architectures for the task of multi-class
segmentation of the lungs field, heart and clavicles in a chest radiograph. In
addition, we explore the influence of using different loss functions in the
training process of a neural network for semantic segmentation. We evaluate all
models on a common benchmark of 247 X-ray images from the JSRT database and
ground-truth segmentation masks from the SCR dataset. Our best performing
architecture, is a modified U-Net that benefits from pre-trained encoder
weights. This model outperformed the current state-of-the-art methods tested on
the same benchmark, with Jaccard overlap scores of 96.1% for lung fields, 90.6%
for heart and 85.5% for clavicles.Comment: Presented at the First International Workshop on Thoracic Image
Analysis (TIA), MICCAI 201
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
Towards automatic pulmonary nodule management in lung cancer screening with deep learning
The introduction of lung cancer screening programs will produce an
unprecedented amount of chest CT scans in the near future, which radiologists
will have to read in order to decide on a patient follow-up strategy. According
to the current guidelines, the workup of screen-detected nodules strongly
relies on nodule size and nodule type. In this paper, we present a deep
learning system based on multi-stream multi-scale convolutional networks, which
automatically classifies all nodule types relevant for nodule workup. The
system processes raw CT data containing a nodule without the need for any
additional information such as nodule segmentation or nodule size and learns a
representation of 3D data by analyzing an arbitrary number of 2D views of a
given nodule. The deep learning system was trained with data from the Italian
MILD screening trial and validated on an independent set of data from the
Danish DLCST screening trial. We analyze the advantage of processing nodules at
multiple scales with a multi-stream convolutional network architecture, and we
show that the proposed deep learning system achieves performance at classifying
nodule type that surpasses the one of classical machine learning approaches and
is within the inter-observer variability among four experienced human
observers.Comment: Published on Scientific Report
Lumbar spine segmentation in MR images: a dataset and a public benchmark
This paper presents a large publicly available multi-center lumbar spine
magnetic resonance imaging (MRI) dataset with reference segmentations of
vertebrae, intervertebral discs (IVDs), and spinal canal. The dataset includes
447 sagittal T1 and T2 MRI series from 218 patients with a history of low back
pain. It was collected from four different hospitals and was divided into a
training (179 patients) and validation (39 patients) set. An iterative data
annotation approach was used by training a segmentation algorithm on a small
part of the dataset, enabling semi-automatic segmentation of the remaining
images. The algorithm provided an initial segmentation, which was subsequently
reviewed, manually corrected, and added to the training data. We provide
reference performance values for this baseline algorithm and nnU-Net, which
performed comparably. We set up a continuous segmentation challenge to allow
for a fair comparison of different segmentation algorithms. This study may
encourage wider collaboration in the field of spine segmentation, and improve
the diagnostic value of lumbar spine MRI
Fifty years of computer analysis in chest imaging: rule-based, machine learning, deep learning
Adversarial Attack Vulnerability of Medical Image Analysis Systems: Unexplored Factors
Adversarial attacks are considered a potentially serious security threat for
machine learning systems. Medical image analysis (MedIA) systems have recently
been argued to be vulnerable to adversarial attacks due to strong financial
incentives and the associated technological infrastructure.
In this paper, we study previously unexplored factors affecting adversarial
attack vulnerability of deep learning MedIA systems in three medical domains:
ophthalmology, radiology, and pathology. We focus on adversarial black-box
settings, in which the attacker does not have full access to the target model
and usually uses another model, commonly referred to as surrogate model, to
craft adversarial examples. We consider this to be the most realistic scenario
for MedIA systems.
Firstly, we study the effect of weight initialization (ImageNet vs. random)
on the transferability of adversarial attacks from the surrogate model to the
target model. Secondly, we study the influence of differences in development
data between target and surrogate models. We further study the interaction of
weight initialization and data differences with differences in model
architecture. All experiments were done with a perturbation degree tuned to
ensure maximal transferability at minimal visual perceptibility of the attacks.
Our experiments show that pre-training may dramatically increase the
transferability of adversarial examples, even when the target and surrogate's
architectures are different: the larger the performance gain using
pre-training, the larger the transferability. Differences in the development
data between target and surrogate models considerably decrease the performance
of the attack; this decrease is further amplified by difference in the model
architecture. We believe these factors should be considered when developing
security-critical MedIA systems planned to be deployed in clinical practice.Comment: First three authors contributed equall
Muon Colliders
Muon Colliders have unique technical and physics advantages and disadvantages
when compared with both hadron and electron machines. They should thus be
regarded as complementary. Parameters are given of 4 TeV and 0.5 TeV high
luminosity \mumu colliders, and of a 0.5 TeV lower luminosity demonstration
machine. We discuss the various systems in such muon colliders, starting from
the proton accelerator needed to generate the muons and proceeding through muon
cooling, acceleration and storage in a collider ring. Problems of detector
background are also discussed.Comment: 28 pages, with 12 postscript figures. To be published Proceedings of
the 9th Advanced ICFA Beam Dynamics Workshop, AIP Pres
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