134 research outputs found
View-Disentangled Transformer for Brain Lesion Detection
Deep neural networks (DNNs) have been widely adopted in brain lesion
detection and segmentation. However, locating small lesions in 2D MRI slices is
challenging, and requires to balance between the granularity of 3D context
aggregation and the computational complexity. In this paper, we propose a novel
view-disentangled transformer to enhance the extraction of MRI features for
more accurate tumour detection. First, the proposed transformer harvests
long-range correlation among different positions in a 3D brain scan. Second,
the transformer models a stack of slice features as multiple 2D views and
enhance these features view-by-view, which approximately achieves the 3D
correlation computing in an efficient way. Third, we deploy the proposed
transformer module in a transformer backbone, which can effectively detect the
2D regions surrounding brain lesions. The experimental results show that our
proposed view-disentangled transformer performs well for brain lesion detection
on a challenging brain MRI dataset.Comment: International Symposium on Biomedical Imaging (ISBI) 2022, code:
https://github.com/lhaof/ISBI-VDForme
Deep Neural Network with l2-norm Unit for Brain Lesions Detection
Automated brain lesions detection is an important and very challenging
clinical diagnostic task because the lesions have different sizes, shapes,
contrasts, and locations. Deep Learning recently has shown promising progress
in many application fields, which motivates us to apply this technology for
such important problem. In this paper, we propose a novel and end-to-end
trainable approach for brain lesions classification and detection by using deep
Convolutional Neural Network (CNN). In order to investigate the applicability,
we applied our approach on several brain diseases including high and low-grade
glioma tumor, ischemic stroke, Alzheimer diseases, by which the brain Magnetic
Resonance Images (MRI) have been applied as an input for the analysis. We
proposed a new operating unit which receives features from several projections
of a subset units of the bottom layer and computes a normalized l2-norm for
next layer. We evaluated the proposed approach on two different CNN
architectures and number of popular benchmark datasets. The experimental
results demonstrate the superior ability of the proposed approach.Comment: Accepted for presentation in ICONIP-201
A Radiomics Approach to Traumatic Brain Injury Prediction in CT Scans
Computer Tomography (CT) is the gold standard technique for brain damage
evaluation after acute Traumatic Brain Injury (TBI). It allows identification
of most lesion types and determines the need of surgical or alternative
therapeutic procedures. However, the traditional approach for lesion
classification is restricted to visual image inspection. In this work, we
characterize and predict TBI lesions by using CT-derived radiomics descriptors.
Relevant shape, intensity and texture biomarkers characterizing the different
lesions are isolated and a lesion predictive model is built by using Partial
Least Squares. On a dataset containing 155 scans (105 train, 50 test) the
methodology achieved 89.7 % accuracy over the unseen data. When a model was
build using only texture features, a 88.2 % accuracy was obtained. Our results
suggest that selected radiomics descriptors could play a key role in brain
injury prediction. Besides, the proposed methodology is close to reproduce
radiologists decision making. These results open new possibilities for
radiomics-inspired brain lesion detection, segmentation and prediction.Comment: Submitted to ISBI 201
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