4,309 research outputs found
A Multi-task Framework for Skin Lesion Detection and Segmentation
Early detection and segmentation of skin lesions is crucial for timely
diagnosis and treatment, necessary to improve the survival rate of patients.
However, manual delineation is time consuming and subject to intra- and
inter-observer variations among dermatologists. This underlines the need for an
accurate and automatic approach to skin lesion segmentation. To tackle this
issue, we propose a multi-task convolutional neural network (CNN) based, joint
detection and segmentation framework, designed to initially localize the lesion
and subsequently, segment it. A `Faster region-based convolutional neural
network' (Faster-RCNN) which comprises a region proposal network (RPN), is used
to generate bounding boxes/region proposals, for lesion localization in each
image. The proposed regions are subsequently refined using a softmax classifier
and a bounding-box regressor. The refined bounding boxes are finally cropped
and segmented using `SkinNet', a modified version of U-Net. We trained and
evaluated the performance of our network, using the ISBI 2017 challenge and the
PH2 datasets, and compared it with the state-of-the-art, using the official
test data released as part of the challenge for the former. Our approach
outperformed others in terms of Dice coefficients (), Jaccard index
(), accuracy () and sensitivity (), across five-fold cross
validation experiments.Comment: Accepted in ISIC-MICCAI 2018 Worksho
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
Detector-SegMentor Network for Skin Lesion Localization and Segmentation
Melanoma is a life-threatening form of skin cancer when left undiagnosed at
the early stages. Although there are more cases of non-melanoma cancer than
melanoma cancer, melanoma cancer is more deadly. Early detection of melanoma is
crucial for the timely diagnosis of melanoma cancer and prohibit its spread to
distant body parts. Segmentation of skin lesion is a crucial step in the
classification of melanoma cancer from the cancerous lesions in dermoscopic
images. Manual segmentation of dermoscopic skin images is very time consuming
and error-prone resulting in an urgent need for an intelligent and accurate
algorithm. In this study, we propose a simple yet novel network-in-network
convolution neural network(CNN) based approach for segmentation of the skin
lesion. A Faster Region-based CNN (Faster RCNN) is used for preprocessing to
predict bounding boxes of the lesions in the whole image which are subsequently
cropped and fed into the segmentation network to obtain the lesion mask. The
segmentation network is a combination of the UNet and Hourglass networks. We
trained and evaluated our models on ISIC 2018 dataset and also cross-validated
on PH\textsuperscript{2} and ISBI 2017 datasets. Our proposed method surpassed
the state-of-the-art with Dice Similarity Coefficient of 0.915 and Accuracy
0.959 on ISIC 2018 dataset and Dice Similarity Coefficient of 0.947 and
Accuracy 0.971 on ISBI 2017 dataset.Comment: 9 pages, 7 figures, accepted at NCVPRIPG 201
Multi-class Semantic Segmentation of Skin Lesions via Fully Convolutional Networks
Melanoma is clinically difficult to distinguish from common benign skin
lesions, particularly melanocytic naevus and seborrhoeic keratosis. The
dermoscopic appearance of these lesions has huge intra-class variations and
high inter-class visual similarities. Most current research is focusing on
single-class segmentation irrespective of classes of skin lesions. In this
work, we evaluate the performance of deep learning on multi-class segmentation
of ISIC-2017 challenge dataset, which consists of 2,750 dermoscopic images. We
propose an end-to-end solution using fully convolutional networks (FCNs) for
multi-class semantic segmentation to automatically segment the melanoma,
seborrhoeic keratosis and naevus. To improve the performance of FCNs, transfer
learning and a hybrid loss function are used. We evaluate the performance of
the deep learning segmentation methods for multi-class segmentation and lesion
diagnosis (with post-processing method) on the testing set of the ISIC-2017
challenge dataset. The results showed that the two-tier level transfer learning
FCN-8s achieved the overall best result with \textit{Dice} score of 78.5% in a
naevus category, 65.3% in melanoma, and 55.7% in seborrhoeic keratosis in
multi-class segmentation and Accuracy of 84.62% for recognition of melanoma in
lesion diagnosis.Comment: Comp2clinic workshop at Biostec 202
A Multi-stage Framework with Context Information Fusion Structure for Skin Lesion Segmentation
The computer-aided diagnosis (CAD) systems can highly improve the reliability
and efficiency of melanoma recognition. As a crucial step of CAD, skin lesion
segmentation has the unsatisfactory accuracy in existing methods due to large
variability in lesion appearance and artifacts. In this work, we propose a
framework employing multi-stage UNets (MS-UNet) in the auto-context scheme to
segment skin lesion accurately end-to-end. We apply two approaches to boost the
performance of MS-UNet. First, UNet is coupled with a context information
fusion structure (CIFS) to integrate the low-level and context information in
the multi-scale feature space. Second, to alleviate the gradient vanishing
problem, we use deep supervision mechanism through supervising MS-UNet by
minimizing a weighted Jaccard distance loss function. Four out of five commonly
used performance metrics, including Jaccard index and Dice coefficient, show
that our approach outperforms the state-ofthe-art deep learning based methods
on the ISBI 2016 Skin Lesion Challenge dataset.Comment: 4 pages, 3 figures, 1 tabl
Global and Local Information Based Deep Network for Skin Lesion Segmentation
With a large influx of dermoscopy images and a growing shortage of
dermatologists, automatic dermoscopic image analysis plays an essential role in
skin cancer diagnosis. In this paper, a new deep fully convolutional neural
network (FCNN) is proposed to automatically segment melanoma out of skin images
by end-to-end learning with only pixels and labels as inputs. Our proposed FCNN
is capable of using both local and global information to segment melanoma by
adopting skipping layers. The public benchmark database consisting of 150
validation images, 600 test images and 2000 training images in the melanoma
detection challenge 2017 at International Symposium Biomedical Imaging 2017 is
used to test the performance of our algorithm. All large size images (for
example, pixels) are reduced to much smaller images with
pixels (more than 10 times smaller). We got and submitted
preliminary results to the challenge without any pre or post processing. The
performance of our proposed method could be further improved by data
augmentation and by avoiding image size reduction.Comment: 4 pages, 3 figures. ISIC201
Generative Adversarial Network in Medical Imaging: A Review
Generative adversarial networks have gained a lot of attention in the
computer vision community due to their capability of data generation without
explicitly modelling the probability density function. The adversarial loss
brought by the discriminator provides a clever way of incorporating unlabeled
samples into training and imposing higher order consistency. This has proven to
be useful in many cases, such as domain adaptation, data augmentation, and
image-to-image translation. These properties have attracted researchers in the
medical imaging community, and we have seen rapid adoption in many traditional
and novel applications, such as image reconstruction, segmentation, detection,
classification, and cross-modality synthesis. Based on our observations, this
trend will continue and we therefore conducted a review of recent advances in
medical imaging using the adversarial training scheme with the hope of
benefiting researchers interested in this technique.Comment: 24 pages; v4; added missing references from before Jan 1st 2019;
accepted to MedI
Region of Interest Detection in Dermoscopic Images for Natural Data-augmentation
With the rapid growth of medical imaging research, there is a great interest
in the automated detection of skin lesions with computer algorithms. The
state-of-the-art datasets for skin lesions are often accompanied with very
limited amount of ground truth labeling as it is laborious and expensive. The
Region Of Interest (ROI) detection is vital to locate the lesion accurately and
must be robust to subtle features of different skin lesion types. In this work,
we propose the use of two object localization meta-architectures for end-to-end
ROI skin lesion detection in dermoscopic images. We trained the
Faster-RCNN-InceptionV2 and SSD-InceptionV2 on the ISBI-2017 training dataset
and evaluated their performance on the ISBI-2017 testing set, PH2 and HAM10000
datasets. Since there was no earlier work in ROI detection for skin lesion with
CNNs, we compared the performance of skin localization methods with the
state-of-the-art segmentation method. The localization methods proved superior
to the segmentation method in ROI detection on skin lesion datasets. In
addition, based on the detected ROI, an automated natural data-augmentation
method is proposed and used as pre-processing in the lesion diagnosis and
segmentation task. To further demonstrate the potential of our work, we
developed a real-time smart-phone application for automated skin lesions
detection.Comment: Natural Augmentatio
Multiple Abnormality Detection for Automatic Medical Image Diagnosis Using Bifurcated Convolutional Neural Network
Automating classification and segmentation process of abnormal regions in
different body organs has a crucial role in most of medical imaging
applications such as funduscopy, endoscopy, and dermoscopy. Detecting multiple
abnormalities in each type of images is necessary for better and more accurate
diagnosis procedure and medical decisions. In recent years portable medical
imaging devices such as capsule endoscopy and digital dermatoscope have been
introduced and made the diagnosis procedure easier and more efficient. However,
these portable devices have constrained power resources and limited
computational capability. To address this problem, we propose a bifurcated
structure for convolutional neural networks performing both classification and
segmentation of multiple abnormalities simultaneously. The proposed network is
first trained by each abnormality separately. Then the network is trained using
all abnormalities. In order to reduce the computational complexity, the network
is redesigned to share some features which are common among all abnormalities.
Later, these shared features are used in different settings (directions) to
segment and classify the abnormal region of the image. Finally, results of the
classification and segmentation directions are fused to obtain the classified
segmentation map. Proposed framework is simulated using four frequent
gastrointestinal abnormalities as well as three dermoscopic lesions and for
evaluation of the proposed framework the results are compared with the
corresponding ground truth map. Properties of the bifurcated network like low
complexity and resource sharing make it suitable to be implemented as a part of
portable medical imaging devices
Fully Convolutional Neural Networks to Detect Clinical Dermoscopic Features
The presence of certain clinical dermoscopic features within a skin lesion
may indicate melanoma, and automatically detecting these features may lead to
more quantitative and reproducible diagnoses. We reformulate the task of
classifying clinical dermoscopic features within superpixels as a segmentation
problem, and propose a fully convolutional neural network to detect clinical
dermoscopic features from dermoscopy skin lesion images. Our neural network
architecture uses interpolated feature maps from several intermediate network
layers, and addresses imbalanced labels by minimizing a negative multi-label
Dice-F score, where the score is computed across the mini-batch for each
label. Our approach ranked first place in the 2017 ISIC-ISBI Part 2:
Dermoscopic Feature Classification Task challenge over both the provided
validation and test datasets, achieving a 0.895% area under the receiver
operator characteristic curve score. We show how simple baseline models can
outrank state-of-the-art approaches when using the official metrics of the
challenge, and propose to use a fuzzy Jaccard Index that ignores the empty set
(i.e., masks devoid of positive pixels) when ranking models. Our results
suggest that (i) the classification of clinical dermoscopic features can be
effectively approached as a segmentation problem, and (ii) the current metrics
used to rank models may not well capture the efficacy of the model. We plan to
make our trained model and code publicly available.Comment: Accepted JBHI versio
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