1,862 research outputs found
Building Disease Detection Algorithms with Very Small Numbers of Positive Samples
Although deep learning can provide promising results in medical image
analysis, the lack of very large annotated datasets confines its full
potential. Furthermore, limited positive samples also create unbalanced
datasets which limit the true positive rates of trained models. As unbalanced
datasets are mostly unavoidable, it is greatly beneficial if we can extract
useful knowledge from negative samples to improve classification accuracy on
limited positive samples. To this end, we propose a new strategy for building
medical image analysis pipelines that target disease detection. We train a
discriminative segmentation model only on normal images to provide a source of
knowledge to be transferred to a disease detection classifier. We show that
using the feature maps of a trained segmentation network, deviations from
normal anatomy can be learned by a two-class classification network on an
extremely unbalanced training dataset with as little as one positive for 17
negative samples. We demonstrate that even though the segmentation network is
only trained on normal cardiac computed tomography images, the resulting
feature maps can be used to detect pericardial effusion and cardiac septal
defects with two-class convolutional classification networks
Error Corrective Boosting for Learning Fully Convolutional Networks with Limited Data
Training deep fully convolutional neural networks (F-CNNs) for semantic image
segmentation requires access to abundant labeled data. While large datasets of
unlabeled image data are available in medical applications, access to manually
labeled data is very limited. We propose to automatically create auxiliary
labels on initially unlabeled data with existing tools and to use them for
pre-training. For the subsequent fine-tuning of the network with manually
labeled data, we introduce error corrective boosting (ECB), which emphasizes
parameter updates on classes with lower accuracy. Furthermore, we introduce
SkipDeconv-Net (SD-Net), a new F-CNN architecture for brain segmentation that
combines skip connections with the unpooling strategy for upsampling. The
SD-Net addresses challenges of severe class imbalance and errors along
boundaries. With application to whole-brain MRI T1 scan segmentation, we
generate auxiliary labels on a large dataset with FreeSurfer and fine-tune on
two datasets with manual annotations. Our results show that the inclusion of
auxiliary labels and ECB yields significant improvements. SD-Net segments a 3D
scan in 7 secs in comparison to 30 hours for the closest multi-atlas
segmentation method, while reaching similar performance. It also outperforms
the latest state-of-the-art F-CNN models.Comment: Accepted at MICCAI 201
Soil-Structure Interaction on the Response of Jacket Type Offshore Wind Turbine
Jacket structures are still at the early stage of their development for use in the offshore wind industry. The aim of this paper is to investigate the effect of the soil-structure interaction on the response of an offshore wind turbine with a jacket-type foundation. For this purpose, two different models of flexible foundation-the p-y model and the p-y model considering pile groups effect-are employed to compare the dynamic responses with the fixed-base model. The modal analysis and the coupled dynamic analysis are carried out under deterministic and stochastic conditions. The influence of the soil-structure interaction on the response of the jacket foundation predicts that the flexible foundation model is necessary to estimate the loads of the offshore wind turbine structure well. It is suggested that during fatigue analysis the pile group effect should be considered for the jacket foundation.None1174Ysciescopu
Deep Lesion Graphs in the Wild: Relationship Learning and Organization of Significant Radiology Image Findings in a Diverse Large-scale Lesion Database
Radiologists in their daily work routinely find and annotate significant
abnormalities on a large number of radiology images. Such abnormalities, or
lesions, have collected over years and stored in hospitals' picture archiving
and communication systems. However, they are basically unsorted and lack
semantic annotations like type and location. In this paper, we aim to organize
and explore them by learning a deep feature representation for each lesion. A
large-scale and comprehensive dataset, DeepLesion, is introduced for this task.
DeepLesion contains bounding boxes and size measurements of over 32K lesions.
To model their similarity relationship, we leverage multiple supervision
information including types, self-supervised location coordinates and sizes.
They require little manual annotation effort but describe useful attributes of
the lesions. Then, a triplet network is utilized to learn lesion embeddings
with a sequential sampling strategy to depict their hierarchical similarity
structure. Experiments show promising qualitative and quantitative results on
lesion retrieval, clustering, and classification. The learned embeddings can be
further employed to build a lesion graph for various clinically useful
applications. We propose algorithms for intra-patient lesion matching and
missing annotation mining. Experimental results validate their effectiveness.Comment: Accepted by CVPR2018. DeepLesion url adde
First measurement of the T-violating muon polarization in the decay K^+ --> mu^+ nu gamma
We present the result of the first measurement of the T-violating muon
polarization P_T in the decay K^+ --> mu^+ nu gamma. This polarization is
sensitive to new sources of CP-violation in the Higgs sector. Using data
accumulated in the period 1996-98 we have obtained P_T = (-0.64 +- 1.85(stat)
+- 0.10(syst))x10^{-2} which is consistent with no T-violation in this decay.Comment: 11 pages, 8 figure
Protein disulfide-isomerase interacts with a substrate protein at all stages along its folding pathway
In contrast to molecular chaperones that couple protein folding to ATP hydrolysis, protein disulfide-isomerase (PDI) catalyzes protein folding coupled to formation of disulfide bonds (oxidative folding). However, we do not know how PDI distinguishes folded, partly-folded and unfolded protein substrates. As a model intermediate in an oxidative folding pathway, we prepared a two-disulfide mutant of basic pancreatic trypsin inhibitor (BPTI) and showed by NMR that it is partly-folded and highly dynamic. NMR studies show that it binds to PDI at the same site that binds peptide ligands, with rapid binding and dissociation kinetics; surface plasmon resonance shows its interaction with PDI has a Kd of ca. 10−5 M. For comparison, we characterized the interactions of PDI with native BPTI and fully-unfolded BPTI. Interestingly, PDI does bind native BPTI, but binding is quantitatively weaker than with partly-folded and unfolded BPTI. Hence PDI recognizes and binds substrates via permanently or transiently unfolded regions. This is the first study of PDI's interaction with a partly-folded protein, and the first to analyze this folding catalyst's changing interactions with substrates along an oxidative folding pathway. We have identified key features that make PDI an effective catalyst of oxidative protein folding – differential affinity, rapid ligand exchange and conformational flexibility
Circulating microRNAs as Specific Biomarkers for Breast Cancer Detection
Background: We previously showed microRNAs (miRNAs) in plasma are potential biomarkers for colorectal cancer detection. Here, we aimed to develop specific blood-based miRNA assay for breast cancer detection. Methodology/Principal Findings: TaqMan-based miRNA profiling was performed in tumor, adjacent non-tumor, corresponding plasma from breast cancer patients, and plasma from matched healthy controls. All putative markers identified were verified in a training set of breast cancer patients. Selected markers were validated in a case-control cohort of 170 breast cancer patients, 100 controls, and 95 other types of cancers and then blindly validated in an independent set of 70 breast cancer patients and 50 healthy controls. Profiling results showed 8 miRNAs were concordantly up-regulated and 1 miRNA was concordantly down-regulated in both plasma and tumor tissue of breast cancer patients. Of the 8 up-regulated miRNAs, only 3 were significantly elevated (p<0.0001) before surgery and reduced after surgery in the training set. Results from the validation cohort showed that a combination of miR-145 and miR-451 was the best biomarker (p<0.0001) in discriminating breast cancer from healthy controls and all other types of cancers. In the blind validation, these plasma markers yielded Receiver Operating Characteristic (ROC) curve area of 0.931. The positive predictive value was 88% and the negative predictive value was 92%. Altered levels of these miRNAs in plasma have been detected not only in advanced stages but also early stages of tumors. The positive predictive value for ductal carcinoma in situ (DCIS) cases was 96%. Conclusions: These results suggested that these circulating miRNAs could be a potential specific biomarker for breast cancer screening. © 2013 Ng et al.published_or_final_versio
Left Ventricle Quantification Using Direct Regression with Segmentation Regularization and Ensembles of Pretrained 2D and 3D CNNs
Cardiac left ventricle (LV) quantification provides a tool for diagnosing
cardiac diseases. Automatic calculation of all relevant LV indices from cardiac
MR images is an intricate task due to large variations among patients and
deformation during the cardiac cycle. Typical methods are based on segmentation
of the myocardium or direct regression from MR images. To consider cardiac
motion and deformation, recurrent neural networks and spatio-temporal
convolutional neural networks (CNNs) have been proposed. We study an approach
combining state-of-the-art models and emphasizing transfer learning to account
for the small dataset provided for the LVQuan19 challenge. We compare 2D
spatial and 3D spatio-temporal CNNs for LV indices regression and cardiac phase
classification. To incorporate segmentation information, we propose an
architecture-independent segmentation-based regularization. To improve the
robustness further, we employ a search scheme that identifies the optimal
ensemble from a set of architecture variants. Evaluating on the LVQuan19
Challenge training dataset with 5-fold cross-validation, we achieve mean
absolute errors of 111 +- 76mm^2, 1.84 +- 0.9mm and 1.22 +- 0.6mm for area,
dimension and regional wall thickness regression, respectively. The error rate
for cardiac phase classification is 6.7%.Comment: Accepted at the MICCAI Workshop STACOM 201
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