30,750 research outputs found
Self-Transfer Learning for Fully Weakly Supervised Object Localization
Recent advances of deep learning have achieved remarkable performances in
various challenging computer vision tasks. Especially in object localization,
deep convolutional neural networks outperform traditional approaches based on
extraction of data/task-driven features instead of hand-crafted features.
Although location information of region-of-interests (ROIs) gives good prior
for object localization, it requires heavy annotation efforts from human
resources. Thus a weakly supervised framework for object localization is
introduced. The term "weakly" means that this framework only uses image-level
labeled datasets to train a network. With the help of transfer learning which
adopts weight parameters of a pre-trained network, the weakly supervised
learning framework for object localization performs well because the
pre-trained network already has well-trained class-specific features. However,
those approaches cannot be used for some applications which do not have
pre-trained networks or well-localized large scale images. Medical image
analysis is a representative among those applications because it is impossible
to obtain such pre-trained networks. In this work, we present a "fully" weakly
supervised framework for object localization ("semi"-weakly is the counterpart
which uses pre-trained filters for weakly supervised localization) named as
self-transfer learning (STL). It jointly optimizes both classification and
localization networks simultaneously. By controlling a supervision level of the
localization network, STL helps the localization network focus on correct ROIs
without any types of priors. We evaluate the proposed STL framework using two
medical image datasets, chest X-rays and mammograms, and achieve signiticantly
better localization performance compared to previous weakly supervised
approaches.Comment: 9 pages, 4 figure
Deep learning architectures for automated image segmentation
Image segmentation is widely used in a variety of computer vision tasks, such
as object localization and recognition, boundary detection, and medical
imaging. This thesis proposes deep learning architectures to improve automatic
object localization and boundary delineation for salient object segmentation in
natural images and for 2D medical image segmentation. First, we propose and
evaluate a novel dilated dense encoder-decoder architecture with a custom
dilated spatial pyramid pooling block to accurately localize and delineate
boundaries for salient object segmentation. The dilation offers better spatial
understanding and the dense connectivity preserves features learned at
shallower levels of the network for better localization. Tested on three
publicly available datasets, our architecture outperforms the state-of-the-art
for one and is very competitive on the other two. Second, we propose and
evaluate a custom 2D dilated dense UNet architecture for accurate lesion
localization and segmentation in medical images. This architecture can be
utilized as a stand-alone segmentation framework or used as a rich feature
extracting backbone to aid other models in medical image segmentation. Our
architecture outperforms all baseline models for accurate lesion localization
and segmentation on a new dataset. We furthermore explore the main
considerations that should be taken into account for 3D medical image
segmentation, among them preprocessing techniques and specialized loss
functions
Retina U-Net: Embarrassingly Simple Exploitation of Segmentation Supervision for Medical Object Detection
The task of localizing and categorizing objects in medical images often
remains formulated as a semantic segmentation problem. This approach, however,
only indirectly solves the coarse localization task by predicting pixel-level
scores, requiring ad-hoc heuristics when mapping back to object-level scores.
State-of-the-art object detectors on the other hand, allow for individual
object scoring in an end-to-end fashion, while ironically trading in the
ability to exploit the full pixel-wise supervision signal. This can be
particularly disadvantageous in the setting of medical image analysis, where
data sets are notoriously small. In this paper, we propose Retina U-Net, a
simple architecture, which naturally fuses the Retina Net one-stage detector
with the U-Net architecture widely used for semantic segmentation in medical
images. The proposed architecture recaptures discarded supervision signals by
complementing object detection with an auxiliary task in the form of semantic
segmentation without introducing the additional complexity of previously
proposed two-stage detectors. We evaluate the importance of full segmentation
supervision on two medical data sets, provide an in-depth analysis on a series
of toy experiments and show how the corresponding performance gain grows in the
limit of small data sets. Retina U-Net yields strong detection performance only
reached by its more complex two-staged counterparts. Our framework including
all methods implemented for operation on 2D and 3D images is available at
github.com/pfjaeger/medicaldetectiontoolkit
Weakly-supervised localization of diabetic retinopathy lesions in retinal fundus images
Convolutional neural networks (CNNs) show impressive performance for image
classification and detection, extending heavily to the medical image domain.
Nevertheless, medical experts are sceptical in these predictions as the
nonlinear multilayer structure resulting in a classification outcome is not
directly graspable. Recently, approaches have been shown which help the user to
understand the discriminative regions within an image which are decisive for
the CNN to conclude to a certain class. Although these approaches could help to
build trust in the CNNs predictions, they are only slightly shown to work with
medical image data which often poses a challenge as the decision for a class
relies on different lesion areas scattered around the entire image. Using the
DiaretDB1 dataset, we show that on retina images different lesion areas
fundamental for diabetic retinopathy are detected on an image level with high
accuracy, comparable or exceeding supervised methods. On lesion level, we
achieve few false positives with high sensitivity, though, the network is
solely trained on image-level labels which do not include information about
existing lesions. Classifying between diseased and healthy images, we achieve
an AUC of 0.954 on the DiaretDB1.Comment: Accepted in Proc. IEEE International Conference on Image Processing
(ICIP), 201
Medical Image Segmentation and Localization using Deformable Templates
This paper presents deformable templates as a tool for segmentation and
localization of biological structures in medical images. Structures are
represented by a prototype template, combined with a parametric warp mapping
used to deform the original shape. The localization procedure is achieved using
a multi-stage, multi-resolution algorithm de-signed to reduce computational
complexity and time. The algorithm initially identifies regions in the image
most likely to contain the desired objects and then examines these regions at
progressively increasing resolutions. The final stage of the algorithm involves
warping the prototype template to match the localized objects. The algorithm is
presented along with the results of four example applications using MRI, x-ray
and ultrasound images.Comment: 4 page
Two-stage framework for optic disc localization and glaucoma classification in retinal fundus images using deep learning
With the advancement of powerful image processing and machine learning
techniques, CAD has become ever more prevalent in all fields of medicine
including ophthalmology. Since optic disc is the most important part of retinal
fundus image for glaucoma detection, this paper proposes a two-stage framework
that first detects and localizes optic disc and then classifies it into healthy
or glaucomatous. The first stage is based on RCNN and is responsible for
localizing and extracting optic disc from a retinal fundus image while the
second stage uses Deep CNN to classify the extracted disc into healthy or
glaucomatous. In addition to the proposed solution, we also developed a
rule-based semi-automatic ground truth generation method that provides
necessary annotations for training RCNN based model for automated disc
localization. The proposed method is evaluated on seven publicly available
datasets for disc localization and on ORIGA dataset, which is the largest
publicly available dataset for glaucoma classification. The results of
automatic localization mark new state-of-the-art on six datasets with accuracy
reaching 100% on four of them. For glaucoma classification we achieved AUC
equal to 0.874 which is 2.7% relative improvement over the state-of-the-art
results previously obtained for classification on ORIGA. Once trained on
carefully annotated data, Deep Learning based methods for optic disc detection
and localization are not only robust, accurate and fully automated but also
eliminates the need for dataset-dependent heuristic algorithms. Our empirical
evaluation of glaucoma classification on ORIGA reveals that reporting only AUC,
for datasets with class imbalance and without pre-defined train and test
splits, does not portray true picture of the classifier's performance and calls
for additional performance metrics to substantiate the results.Comment: 16 Pages, 10 Figure
Thoracic Disease Identification and Localization with Limited Supervision
Accurate identification and localization of abnormalities from radiology
images play an integral part in clinical diagnosis and treatment planning.
Building a highly accurate prediction model for these tasks usually requires a
large number of images manually annotated with labels and finding sites of
abnormalities. In reality, however, such annotated data are expensive to
acquire, especially the ones with location annotations. We need methods that
can work well with only a small amount of location annotations. To address this
challenge, we present a unified approach that simultaneously performs disease
identification and localization through the same underlying model for all
images. We demonstrate that our approach can effectively leverage both class
information as well as limited location annotation, and significantly
outperforms the comparative reference baseline in both classification and
localization tasks.Comment: Conference on Computer Vision and Pattern Recognition 2018 (CVPR
2018). V1: CVPR submission; V2: +supplementary; V3: CVPR camera-ready; V4:
correction, update reference baseline results according to their latest post;
V5: minor correction; V6: Identification results using NIH data splits and
various image model
Deep multiscale convolutional feature learning for weakly supervised localization of chest pathologies in X-ray images
Localization of chest pathologies in chest X-ray images is a challenging task
because of their varying sizes and appearances. We propose a novel weakly
supervised method to localize chest pathologies using class aware deep
multiscale feature learning. Our method leverages intermediate feature maps
from CNN layers at different stages of a deep network during the training of a
classification model using image level annotations of pathologies. During the
training phase, a set of \emph{layer relevance weights} are learned for each
pathology class and the CNN is optimized to perform pathology classification by
convex combination of feature maps from both shallow and deep layers using the
learned weights. During the test phase, to localize the predicted pathology,
the multiscale attention map is obtained by convex combination of class
activation maps from each stage using the \emph{layer relevance weights}
learned during the training phase. We have validated our method using 112000
X-ray images and compared with the state-of-the-art localization methods. We
experimentally demonstrate that the proposed weakly supervised method can
improve the localization performance of small pathologies such as nodule and
mass while giving comparable performance for bigger pathologies e.g.,
Cardiomegal
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
Fast 3D Salient Region Detection in Medical Images using GPUs
Automated detection of visually salient regions is an active area of research
in computer vision. Salient regions can serve as inputs for object detectors as
well as inputs for region based registration algorithms. In this paper we
consider the problem of speeding up computationally intensive bottom-up salient
region detection in 3D medical volumes.The method uses the Kadir Brady
formulation of saliency. We show that in the vicinity of a salient region,
entropy is a monotonically increasing function of the degree of overlap of a
candidate window with the salient region. This allows us to initialize a sparse
seed-point grid as the set of tentative salient region centers and iteratively
converge to the local entropy maxima, thereby reducing the computation
complexity compared to the Kadir Brady approach of performing this computation
at every point in the image. We propose two different approaches for achieving
this. The first approach involves evaluating entropy in the four quadrants
around the seed point and iteratively moving in the direction that increases
entropy. The second approach we propose makes use of mean shift tracking
framework to affect entropy maximizing moves. Specifically, we propose the use
of uniform pmf as the target distribution to seek high entropy regions. We
demonstrate the use of our algorithm on medical volumes for left ventricle
detection in PET images and tumor localization in brain MR sequences.Comment: 9 page
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